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Journal articles on the topic 'Object Proposal Generation'
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Jie, Zequn, Wen Feng Lu, Siavash Sakhavi, Yunchao Wei, Eng Hock Francis Tay, and Shuicheng Yan. "Object Proposal Generation With Fully Convolutional Networks." IEEE Transactions on Circuits and Systems for Video Technology 28, no. 1 (January 2018): 62–75. http://dx.doi.org/10.1109/tcsvt.2016.2576759.
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Deng, Yao, Huawei Liang, and Zhiyan Yi. "An Improved Approach for Object Proposals Generation." Electronics 10, no. 7 (March 27, 2021): 794. http://dx.doi.org/10.3390/electronics10070794.
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The objectness measure is a significant and effective method used for generic object detection. However, several object detection methods can achieve accurate results by using more than 1000 candidate object proposals. In addition, the weight of each proposal is weak and also cannot distinguish object proposals. These weak proposals have brought difficulties to the subsequent analysis. To significantly reduce the number of proposals, this paper presents an improved generic object detection approach, which predicts candidate object proposals from more than 10,000 proposals. All candidate proposals can be divided, rather than preclassified, into three categories: entire object, partial object, and nonobject. These partial object proposals also display fragmentary information of the objectness feature, which can be used to reconstruct the object boundary. By using partial objectness to enhance the weight of the entire object proposals, we removed a huge number of useless proposals and reduced the space occupied by the true positive object proposals. We designed a neural network with lightweight computation to cluster the most possible object proposals with rerank and box regression. Through joint training, the lightweight network can share the features with other subsequent tasks. The proposed method was validated using experiments with the PASCAL VOC2007 dataset. The results showed that the proposed approach was significantly improved compared with the existing methods and can accurately detect 92.3% of the objects by using less than 200 proposals.
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Chen, Xiaozhi, Huimin Ma, Chenzhuo Zhu, Xiang Wang, and Zhichen Zhao. "Boundary-aware box refinement for object proposal generation." Neurocomputing 219 (January 2017): 323–32. http://dx.doi.org/10.1016/j.neucom.2016.09.045.
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Zhang, Ziming, and Philip H. S. Torr. "Object Proposal Generation Using Two-Stage Cascade SVMs." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 1 (January 1, 2016): 102–15. http://dx.doi.org/10.1109/tpami.2015.2430348.
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Liu, Qian, Feng Yang, and Ce Li. "AWBING plus algorithm for generic object proposal generation." Journal of Intelligent & Fuzzy Systems 36, no. 6 (June 11, 2019): 6685–701. http://dx.doi.org/10.3233/jifs-18810.
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Wang, Congchao, Jufeng Yang, Kai Wang, and Shang-Hong Lai. "Multi-scale energy optimization for object proposal generation." Multimedia Tools and Applications 76, no. 8 (May 23, 2016): 10481–99. http://dx.doi.org/10.1007/s11042-016-3616-7.
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Feng, Yiliu, Wanzeng Cai, Xiaolong Liu, Huini Fu, Yafei Liu, and Hengzhu Liu. "Improved Object Proposals with Geometrical Features for Autonomous Driving." Mobile Information Systems 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3175186.
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This paper aims at generating high-quality object proposals for object detection in autonomous driving. Most existing proposal generation methods are designed for the general object detection, which may not perform well in a particular scene. We propose several geometrical features suited for autonomous driving and integrate them into state-of-the-art general proposal generation methods. In particular, we formulate the integration as a feature fusion problem by fusing the geometrical features with existing proposal generation methods in a Bayesian framework. Experiments on the challenging KITTI benchmark demonstrate that our approach improves the existing methods significantly. Combined with a convolutional neural net detector, our approach achieves state-of-the-art performance on all three KITTI object classes.
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Zhang, Ziming, Yun Liu, Xi Chen, Yanjun Zhu, Ming-Ming Cheng, Venkatesh Saligrama, and Philip H. S. Torr. "Sequential Optimization for Efficient High-Quality Object Proposal Generation." IEEE Transactions on Pattern Analysis and Machine Intelligence 40, no. 5 (May 1, 2018): 1209–23. http://dx.doi.org/10.1109/tpami.2017.2707492.
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Sun, Ning, Feng Jiang, Hengchao Yan, Jixin Liu, and Guang Han. "Proposal generation method for object detection in infrared image." Infrared Physics & Technology 81 (March 2017): 117–27. http://dx.doi.org/10.1016/j.infrared.2016.12.021.
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Wang, Juan, Xiaoming Tao, Mai Xu, Yiping Duan, and Jianhua Lu. "Hierarchical objectness network for region proposal generation and object detection." Pattern Recognition 83 (November 2018): 260–72. http://dx.doi.org/10.1016/j.patcog.2018.05.009.
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Pont-Tuset, Jordi, Pablo Arbelaez, Jonathan T.Barron, Ferran Marques, and Jitendra Malik. "Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation." IEEE Transactions on Pattern Analysis and Machine Intelligence 39, no. 1 (January 1, 2017): 128–40. http://dx.doi.org/10.1109/tpami.2016.2537320.
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Jiao, Lin, Shengyu Zhang, Shifeng Dong, and Hongqiang Wang. "RFP-Net: Receptive field-based proposal generation network for object detection." Neurocomputing 405 (September 2020): 138–48. http://dx.doi.org/10.1016/j.neucom.2020.04.106.
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Dong, Ruchan, Licheng Jiao, Yan Zhang, Jin Zhao, and Weiyan Shen. "A Multi-Scale Spatial Attention Region Proposal Network for High-Resolution Optical Remote Sensing Imagery." Remote Sensing 13, no. 17 (August 25, 2021): 3362. http://dx.doi.org/10.3390/rs13173362.
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Deep convolutional neural networks (DCNNs) are driving progress in object detection of high-resolution remote sensing images. Region proposal generation, as one of the key steps in object detection, has also become the focus of research. High-resolution remote sensing images usually contain various sizes of objects and complex background, small objects are easy to miss or be mis-identified in object detection. If the recall rate of region proposal of small objects and multi-scale objects can be improved, it will bring an improvement on the performance of the accuracy in object detection. Spatial attention is the ability to focus on local features in images and can improve the learning efficiency of DCNNs. This study proposes a multi-scale spatial attention region proposal network (MSA-RPN) for high-resolution optical remote sensing imagery. The MSA-RPN is an end-to-end deep learning network with a backbone network of ResNet. It deploys three novel modules to fulfill its task. First, the Scale-specific Feature Gate (SFG) focuses on features of objects by processing multi-scale features extracted from the backbone network. Second, the spatial attention-guided model (SAGM) obtains spatial information of objects from the multi-scale attention maps. Third, the Selective Strong Attention Maps Model (SSAMM) adaptively selects sliding windows according to the loss values from the system’s feedback, and sends the windowed samples to the spatial attention decoder. Finally, the candidate regions and their corresponding confidences can be obtained. We evaluate the proposed network in a public dataset LEVIR and compare with several state-of-the-art methods. The proposed MSA-RPN yields a higher recall rate of region proposal generation, especially for small targets in remote sensing images.
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Wang, Hui, Hao Li, Wanli Qian, Wenhui Diao, Liangjin Zhao, Jinghua Zhang, and Daobing Zhang. "Dynamic Pseudo-Label Generation for Weakly Supervised Object Detection in Remote Sensing Images." Remote Sensing 13, no. 8 (April 10, 2021): 1461. http://dx.doi.org/10.3390/rs13081461.
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In recent years, fully supervised object detection methods in remote sensing images with good performance have been developed. However, this approach requires a large number of instance-level annotated samples that are relatively expensive to acquire. Therefore, weakly supervised learning using only image-level annotations has attracted much attention. Most of the weakly supervised object detection methods are based on multi-instance learning methods, and their performance depends on the process of scoring the candidate region proposals during training. In this process, the use of only image-level labels for supervision usually cannot obtain optimal results due to the lack of location information of the object. To address the above problem, a dynamic sample pseudo-label generation framework is proposed to generate pseudo-labels for each proposal without additional annotations. First, we propose the pseudo-label generation algorithm (PLG) to generate the category labels of the proposal by using the localization information of the object. Specifically, we propose to use the pixel average of the object’s localization map in the proposal as the proposal category confidence and calculate the pseudo-label by comparing the proposal category confidence with the preset threshold. In addition, an effective adaptive threshold selection strategy is designed to eliminate the effect of different category shape differences in computing sample pseudo-labels. Comparative experiments on the NWPU VHR-10 dataset demonstrate that our method can significantly improve the detection performance compared to existing methods.
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Wang, Juncheng, and Guiying Li. "Accelerate proposal generation in R-CNN methods for fast pedestrian extraction." Electronic Library 37, no. 3 (June 3, 2019): 435–53. http://dx.doi.org/10.1108/el-09-2018-0191.
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Purpose The purpose of this study is to develop a novel region-based convolutional neural networks (R-CNN) approach that is more efficient while at least as accurate as existing R-CNN methods. In this way, the proposed method, namely R2-CNN, provides a more powerful tool for pedestrian extraction for person re-identification, which involve a huge number of images and pedestrian needs to be extracted efficiently to meet the real-time requirement. Design/methodology/approach The proposed R2-CNN is tested on two types of data sets. The first one the USC Pedestrian Detection data set, which consists of three sub-sets USC-A, UCS-B and USC-C, with respect to their characteristics. This data set is used to test the performance of R2-CNN in the pedestrian extraction task. The speed and performance of the investigated algorithms were collected. The second data set is the PASCAL VOC 2007 data set, which is a common benchmark data set for object detection. This data set was used to analyze characteristics of R2-CNN in the case of general object detection task. Findings This study proposes a novel R-CNN method that is both more efficient and more accurate than existing methods. The method, when used as an object detector, would facilitate the data preprocessing stage of person re-identification. Originality/value The study proposes a novel approach for object detection, which shows advantages in both efficiency and accuracy for pedestrian detection task. It contributes to both data preprocessing for person re-identification and the research on deep learning.
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Liu, Fang, Liang Zhao, Xiaochun Cheng, Qin Dai, Xiangbin Shi, and Jianzhong Qiao. "Fine-Grained Action Recognition by Motion Saliency and Mid-Level Patches." Applied Sciences 10, no. 8 (April 18, 2020): 2811. http://dx.doi.org/10.3390/app10082811.
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Effective extraction of human body parts and operated objects participating in action is the key issue of fine-grained action recognition. However, most of the existing methods require intensive manual annotation to train the detectors of these interaction components. In this paper, we represent videos by mid-level patches to avoid the manual annotation, where each patch corresponds to an action-related interaction component. In order to capture mid-level patches more exactly and rapidly, candidate motion regions are extracted by motion saliency. Firstly, the motion regions containing interaction components are segmented by a threshold adaptively calculated according to the saliency histogram of the motion saliency map. Secondly, we introduce a mid-level patch mining algorithm for interaction component detection, with object proposal generation and mid-level patch detection. The object proposal generation algorithm is used to obtain multi-granularity object proposals inspired by the idea of the Huffman algorithm. Based on these object proposals, the mid-level patch detectors are trained by K-means clustering and SVM. Finally, we build a fine-grained action recognition model using a graph structure to describe relationships between the mid-level patches. To recognize actions, the proposed model calculates the appearance and motion features of mid-level patches and the binary motion cooperation relationships between adjacent patches in the graph. Extensive experiments on the MPII cooking database demonstrate that the proposed method gains better results on fine-grained action recognition.
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Li, Xuesong, Jose Guivant, and Subhan Khan. "Real-time 3D object proposal generation and classification using limited processing resources." Robotics and Autonomous Systems 130 (August 2020): 103557. http://dx.doi.org/10.1016/j.robot.2020.103557.
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Guan, Yurong, Muhammad Aamir, Zhihua Hu, Waheed Ahmed Abro, Ziaur Rahman, Zaheer Ahmed Dayo, and Shakeel Akram. "A Region-Based Efficient Network for Accurate Object Detection." Traitement du Signal 38, no. 2 (April 30, 2021): 481–94. http://dx.doi.org/10.18280/ts.380228.
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Object detection in images is an important task in image processing and computer vision. Many approaches are available for object detection. For example, there are numerous algorithms for object positioning and classification in images. However, the current methods perform poorly and lack experimental verification. Thus, it is a fascinating and challenging issue to position and classify image objects. Drawing on the recent advances in image object detection, this paper develops a region-baed efficient network for accurate object detection in images. To improve the overall detection performance, image object detection was treated as a twofold problem, involving object proposal generation and object classification. First, a framework was designed to generate high-quality, class-independent, accurate proposals. Then, these proposals, together with their input images, were imported to our network to learn convolutional features. To boost detection efficiency, the number of proposals was reduced by a network refinement module, leaving only a few eligible candidate proposals. After that, the refined candidate proposals were loaded into the detection module to classify the objects. The proposed model was tested on the test set of the famous PASCAL Visual Object Classes Challenge 2007 (VOC2007). The results clearly demonstrate that our model achieved robust overall detection efficiency over existing approaches using fewer or more proposals, in terms of recall, mean average best overlap (MABO), and mean average precision (mAP).
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Bai, Xiang, Zheng Zhang, Hong-Yang Wang, and Wei Shen. "Directional Edge Boxes: Exploiting Inner Normal Direction Cues for Effective Object Proposal Generation." Journal of Computer Science and Technology 32, no. 4 (July 2017): 701–13. http://dx.doi.org/10.1007/s11390-017-1752-9.
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Jung, Gayoung, Jonghun Lee, and Incheol Kim. "Tracklet Pair Proposal and Context Reasoning for Video Scene Graph Generation." Sensors 21, no. 9 (May 2, 2021): 3164. http://dx.doi.org/10.3390/s21093164.
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Video scene graph generation (ViDSGG), the creation of video scene graphs that helps in deeper and better visual scene understanding, is a challenging task. Segment-based and sliding-window based methods have been proposed to perform this task. However, they all have certain limitations. This study proposes a novel deep neural network model called VSGG-Net for video scene graph generation. The model uses a sliding window scheme to detect object tracklets of various lengths throughout the entire video. In particular, the proposed model presents a new tracklet pair proposal method that evaluates the relatedness of object tracklet pairs using a pretrained neural network and statistical information. To effectively utilize the spatio-temporal context, low-level visual context reasoning is performed using a spatio-temporal context graph and a graph neural network as well as high-level semantic context reasoning. To improve the detection performance for sparse relationships, the proposed model applies a class weighting technique that adjusts the weight of sparse relationships to a higher level. This study demonstrates the positive effect and high performance of the proposed model through experiments using the benchmark dataset VidOR and VidVRD.
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Nguyen, Hoanh. "Predicted Anchor Region Proposal with Balanced Feature Pyramid for License Plate Detection in Traffic Scene Images." Complexity 2020 (June 16, 2020): 1–11. http://dx.doi.org/10.1155/2020/5137056.
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License plate detection is a key problem in intelligent transportation systems. Recently, many deep learning-based networks have been proposed and achieved incredible success in general object detection, such as faster R-CNN, SSD, and R-FCN. However, directly applying these deep general object detection networks on license plate detection without modifying may not achieve good enough performance. This paper proposes a novel deep learning-based framework for license plate detection in traffic scene images based on predicted anchor region proposal and balanced feature pyramid. In the proposed framework, ResNet-34 architecture is first adopted for generating the base convolution feature maps. A balanced feature pyramid generation module is then used to generate balanced feature pyramid, of which each feature level obtains equal information from other feature levels. Furthermore, this paper designs a multiscale region proposal network with a novel predicted location anchor scheme to generate high-quality proposals. Finally, a detection network which includes a region of interest pooling layer and fully connected layers is adopted to further classify and regress the coordinates of detected license plates. Experimental results on public datasets show that the proposed approach achieves better detection performance compared with other state-of-the-art methods on license plate detection.
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Wu, Yingying, Huacheng Qin, Tao Liu, Hao Liu, and Zhiqiang Wei. "A 3D Object Detection Based on Multi-Modality Sensors of USV." Applied Sciences 9, no. 3 (February 5, 2019): 535. http://dx.doi.org/10.3390/app9030535.
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Unmanned Surface Vehicles (USVs) are commonly equipped with multi-modality sensors. Fully utilized sensors could improve object detection of USVs. This could further contribute to better autonomous navigation. The purpose of this paper is to solve the problems of 3D object detection of USVs in complicated marine environment. We propose a 3D object detection Depth Neural Network based on multi-modality data of USVs. This model includes a modified Proposal Generation Network and Deep Fusion Detection Network. The Proposal Generation Network improves feature extraction. Meanwhile, the Deep Fusion Detection Network enhances the fusion performance and can achieve more accurate results of object detection. The model was tested on both the KITTI 3D object detection dataset (A project of Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago) and a self-collected offshore dataset. The model shows excellent performance in a small memory condition. The results further prove that the method based on deep learning can give good accuracy in conditions of complicated surface in marine environment.
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Chen, Qingsheng, Cien Fan, Weizheng Jin, Lian Zou, Fangyu Li, Xiaopeng Li, Hao Jiang, Minyuan Wu, and Yifeng Liu. "EPGNet: Enhanced Point Cloud Generation for 3D Object Detection." Sensors 20, no. 23 (December 4, 2020): 6927. http://dx.doi.org/10.3390/s20236927.
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Three-dimensional object detection from point cloud data is becoming more and more significant, especially for autonomous driving applications. However, it is difficult for lidar to obtain the complete structure of an object in a real scene due to its scanning characteristics. Although the existing methods have made great progress, most of them ignore the prior information of object structure, such as symmetry. So, in this paper, we use the symmetry of the object to complete the missing part in the point cloud and then detect it. Specifically, we propose a two-stage detection framework. In the first stage, we adopt an encoder–decoder structure to generate the symmetry points of the foreground points and make the symmetry points and the non-empty voxel centers form an enhanced point cloud. In the second stage, the enhanced point cloud is input into the baseline, which is an anchor-based region proposal network, to generate the detection results. Extensive experiments on the challenging KITTI benchmark show the effectiveness of our method, which has better performance on both 3D and BEV (bird’s eye view) object detection compared with some previous state-of-the-art methods.
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Dai, Y., J. S. Xiao, B. S. Yi, J. F. Lei, and Z. Y. Du. "DETECTION OF ARTIFICIAL OBJECTS IN REMOTE SENSING IMAGE BASED ON DEEP LEARNING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 321–26. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-321-2020.
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Abstract. Aiming at multi-class artificial object detection in remote sensing images, the detection framework based on deep learning is used to extract and localize the numerous targets existing in very high resolution remote sensing images. In order to realize rapid and efficient detection of the typical artificial targets on the remote sensing image, this paper proposes an end-to-end multi-category object detection method in remote sensing image based on the convolutional neural network to solve several challenges, including dense objects and objects with arbitrary direction and large aspect ratios. Specifically, in this paper, the feature extraction process is improved by utilizing a more advanced backbone network with deeper layers and combining multiple feature maps including the high-resolution features maps with more location details and low-resolution feature maps with highly-abstracted information. And a Rotating Regional Proposal Network is adopted into the Faster R-CNN network to generate candidate object-like regions with different orientations and to improve the sensitivity to dense and cluttered objects. The rotation factor is added into the regional proposal network to control the generation of anchor box’s angle and to cover enough directions of typical man-made objects. Meanwhile, the misalignment caused by the two quantifications operations in the pooling process is eliminated and a convolution layer is appended before the fully connected layer of the final classification network to reduce the feature parameters and avoid overfitting. Compared with current generic object detection method, the proposed algorithm focus on the arbitrary oriented and dense artificial targets in remote sensing images. After comprehensive evaluation with several state-of-the-art object detection algorithms, our method is proved to be effective to detect multi-class artificial object in remote sensing image. Experiments demonstrate that the proposed method combines the powerful features extracted by the improved convolutional neural networks with multi-scale features and rotating region network is more accurate in the public DOTA dataset.
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Xiao, Junsheng, Huahu Xu, Honghao Gao, Minjie Bian, and Yang Li. "A Weakly Supervised Semantic Segmentation Network by Aggregating Seed Cues: The Multi-Object Proposal Generation Perspective." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1s (March 31, 2021): 1–19. http://dx.doi.org/10.1145/3419842.
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Weakly supervised semantic segmentation under image-level annotations is effectiveness for real-world applications. The small and sparse discriminative regions obtained from an image classification network that are typically used as the important initial location of semantic segmentation also form the bottleneck. Although deep convolutional neural networks (DCNNs) have exhibited promising performances for single-label image classification tasks, images of the real-world usually contain multiple categories, which is still an open problem. So, the problem of obtaining high-confidence discriminative regions from multi-label classification networks remains unsolved. To solve this problem, this article proposes an innovative three-step framework within the perspective of multi-object proposal generation. First, an image is divided into candidate boxes using the object proposal method. The candidate boxes are sent to a single-classification network to obtain the discriminative regions. Second, the discriminative regions are aggregated to obtain a high-confidence seed map. Third, the seed cues grow on the feature maps of high-level semantics produced by a backbone segmentation network. Experiments are carried out on the PASCAL VOC 2012 dataset to verify the effectiveness of our approach, which is shown to outperform other baseline image segmentation methods.
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Wang, Ye, Zhenyi Liu, and Weiwen Deng. "Anchor Generation Optimization and Region of Interest Assignment for Vehicle Detection." Sensors 19, no. 5 (March 3, 2019): 1089. http://dx.doi.org/10.3390/s19051089.
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Region proposal network (RPN) based object detection, such as Faster Regions with CNN (Faster R-CNN), has gained considerable attention due to its high accuracy and fast speed. However, it has room for improvements when used in special application situations, such as the on-board vehicle detection. Original RPN locates multiscale anchors uniformly on each pixel of the last feature map and classifies whether an anchor is part of the foreground or background with one pixel in the last feature map. The receptive field of each pixel in the last feature map is fixed in the original faster R-CNN and does not coincide with the anchor size. Hence, only a certain part can be seen for large vehicles and too much useless information is contained in the feature for small vehicles. This reduces detection accuracy. Furthermore, the perspective projection results in the vehicle bounding box size becoming related to the bounding box position, thereby reducing the effectiveness and accuracy of the uniform anchor generation method. This reduces both detection accuracy and computing speed. After the region proposal stage, many regions of interest (ROI) are generated. The ROI pooling layer projects an ROI to the last feature map and forms a new feature map with a fixed size for final classification and box regression. The number of feature map pixels in the projected region can also influence the detection performance but this is not accurately controlled in former works. In this paper, the original faster R-CNN is optimized, especially for the on-board vehicle detection. This paper tries to solve these above-mentioned problems. The proposed method is tested on the KITTI dataset and the result shows a significant improvement without too many tricky parameter adjustments and training skills. The proposed method can also be used on other objects with obvious foreshortening effects, such as on-board pedestrian detection. The basic idea of the proposed method does not rely on concrete implementation and thus, most deep learning based object detectors with multiscale feature maps can be optimized with it.
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Saez-Mingorance, Borja, Antonio Escobar-Molero, Javier Mendez-Gomez, Encarnacion Castillo-Morales, and Diego P. Morales-Santos. "Object Positioning Algorithm Based on Multidimensional Scaling and Optimization for Synthetic Gesture Data Generation." Sensors 21, no. 17 (September 3, 2021): 5923. http://dx.doi.org/10.3390/s21175923.
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This work studies the feasibility of a novel two-step algorithm for infrastructure and object positioning, using pairwise distances. The proposal is based on the optimization algorithms, Scaling-by-Majorizing-a-Complicated-Function and the Limited-Memory-Broyden-Fletcher-Goldfarb-Shannon. A qualitative evaluation of these algorithms is performed for 3D positioning. As the final stage, smoothing filtering techniques are applied to estimate the trajectory, from the previously obtained positions. This approach can also be used as a synthetic gesture data generator framework. This framework is independent from the hardware and can be used to simulate the estimation of trajectories from noisy distances gathered with a large range of sensors by modifying the noise properties of the initial distances. The framework is validated, using a system of ultrasound transceivers. The results show this framework to be an efficient and simple positioning and filtering approach, accurately reconstructing the real path followed by the mobile object while maintaining low latency. Furthermore, these capabilities can be exploited by using the proposed algorithms for synthetic data generation, as demonstrated in this work, where synthetic ultrasound gesture data are generated.
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Liu, Li, Wanli Ouyang, Xiaogang Wang, Paul Fieguth, Jie Chen, Xinwang Liu, and Matti Pietikäinen. "Deep Learning for Generic Object Detection: A Survey." International Journal of Computer Vision 128, no. 2 (October 31, 2019): 261–318. http://dx.doi.org/10.1007/s11263-019-01247-4.
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Abstract Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.
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Malini, A., P. Priyadharshini, and S. Sabeena. "An automatic assessment of road condition from aerial imagery using modified VGG architecture in faster-RCNN framework." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 11411–22. http://dx.doi.org/10.3233/jifs-202596.
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To develop a surveillance and detection system for automating the process of road maintenance work which is being carried out by surveying and inspection of roads manually in the current situation. The need of the system lies in the fact that traditional methods are time-consuming, tiresome and require huge workforce. This paper proposes an automation system using Unmanned Aerial Vehicle which monitors and detects the pavement defects like cracks and potholes by processing real-time video footage of Indian highways. The collected data is processed and stored as images in a road defects database which serves as input for the system. The behavior of Region Proposal Network (RPN) is made smooth by varying the number of region proposals utilized in the model. A regularization technique called dropout is used to achieve higher performance in the proposed Faster Region based Convolutional Neural Networks object detection model. The detections are made with 62.3% mean Average Precision @ Intersection over Union (IoU)> = 0.5 for the generation of 300 region proposals which is a good score for object detections. The comparisons between proposed and existing systems shows that the proposed Faster RCNN with modified VGG-16 performs well than the existing variants.
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SHAVOLKIN, O. O., I. O. SHVEDCHYKOVA, H. V. KRUHLIAK, YE YU STANOVSKYI, and M. O. PIDHAYNYY. "A software-hardware complex for controlling a photoelectric system with a battery to provide for the own needs of a local object connected to the grid." Journal of Electrical and power engineering 23, no. 2 (December 23, 2020): 20–27. http://dx.doi.org/10.31474/2074-2630-2020-2-20-27.
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The software and hardware complex for managing the generation and redistribution of energy in a photovoltaic system with a battery, which provides the own needs of the local object connected to the grid with a three-zone tariffication is presented. A decrease in the cost of paying for electricity consumed from the grid is achieved by using cheaper energy during peak hours with matching the load with the generation of a photovoltaic battery and the degree of charge of the battery. There is a proposal to forming
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Sartori, I., T. H. Dokka, and Inger Andresen. "PROPOSAL OF A NORWEGIAN ZEB DEFINITION: ASSESSING THE IMPLICATIONS FOR DESIGN." Journal of Green Building 6, no. 3 (July 2011): 133–50. http://dx.doi.org/10.3992/jgb.6.3.133.
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Conceptually a Zero Emission Building (ZEB) is a building with greatly reduced energy demand and able to generate electricity (or other carriers) from renewable sources in order to achieve a carbon neutral balance. However, a clear and agreed definition of Zero Emission Building (ZEB) is yet to be achieved, both internationally and in Norway. However, it is understood that both the definition and the surrounding energy supply system will affect significantly the way buildings are designed to achieve the ZEB goal. A formal definition of ZEB is characterized by a set of criteria that are: the system boundary, feeding-in possibilities, balance object, balancing period, credits, crediting method, energy performance and mismatch factors. For each criterion different options are available, and the choice of which options are more appropriate to define ZEBs may depend on the political targets laying behind the promotion of ZEBs, hence may vary from country to country. This paper focuses on two of these criteria: energy performance and credits used to measure the ZEB balance. For each criterion different options are considered and the implications they have on the building design are assessed. The case study is on a typical Norwegian single family house. It is shown that for certain choices on the two criteria options, a paradoxical situation could arise. When using off-site generation based on biomass/biofuels, achieving the ZEB balance may be easier for high energy consuming buildings than for efficient ones. This is the exact opposite of what ZEBs are meant to promote: design of energy efficient buildings with on-site generation options. Recommendations on how to avoid such a paradox are suggested.
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Zhuang, Shuo, Ping Wang, Boran Jiang, Gang Wang, and Cong Wang. "A Single Shot Framework with Multi-Scale Feature Fusion for Geospatial Object Detection." Remote Sensing 11, no. 5 (March 12, 2019): 594. http://dx.doi.org/10.3390/rs11050594.
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With the rapid advances in remote-sensing technologies and the larger number of satellite images, fast and effective object detection plays an important role in understanding and analyzing image information, which could be further applied to civilian and military fields. Recently object detection methods with region-based convolutional neural network have shown excellent performance. However, these two-stage methods contain region proposal generation and object detection procedures, resulting in low computation speed. Because of the expensive manual costs, the quantity of well-annotated aerial images is scarce, which also limits the progress of geospatial object detection in remote sensing. In this paper, on the one hand, we construct and release a large-scale remote-sensing dataset for geospatial object detection (RSD-GOD) that consists of 5 different categories with 18,187 annotated images and 40,990 instances. On the other hand, we design a single shot detection framework with multi-scale feature fusion. The feature maps from different layers are fused together through the up-sampling and concatenation blocks to predict the detection results. High-level features with semantic information and low-level features with fine details are fully explored for detection tasks, especially for small objects. Meanwhile, a soft non-maximum suppression strategy is put into practice to select the final detection results. Extensive experiments have been conducted on two datasets to evaluate the designed network. Results show that the proposed approach achieves a good detection performance and obtains the mean average precision value of 89.0% on a newly constructed RSD-GOD dataset and 83.8% on the Northwestern Polytechnical University very high spatial resolution-10 (NWPU VHR-10) dataset at 18 frames per second (FPS) on a NVIDIA GTX-1080Ti GPU.
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Guo, Qingwen, Chuntao Wang, Deqin Xiao, and Qiong Huang. "An Enhanced Insect Pest Counter Based on Saliency Map and Improved Non-Maximum Suppression." Insects 12, no. 8 (August 6, 2021): 705. http://dx.doi.org/10.3390/insects12080705.
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Accurately counting the number of insect pests from digital images captured on yellow sticky traps remains a challenge in the field of insect pest monitoring. In this study, we develop a new approach to counting the number of insect pests using a saliency map and improved non-maximum suppression. Specifically, as the background of a yellow sticky trap is simple and the insect pest object is small, we exploit a saliency map to construct a region proposal generator including saliency map building, activation region formation, background–foreground classifier, and tune-up boxes involved in region proposal generation. For each region proposal, a convolutional neural network (CNN) model is used to classify it as a specific insect pest class, resulting in detection bounding boxes. By considering the relationship between detection bounding boxes, we thus develop an improved non-maximum suppression to sophisticatedly handle the redundant detection bounding boxes and obtain the insect pest number through counting the handled detection bounding boxes, each of which covers one insect pest. As this insect pest counter may miscount insect pests that are close to each other, we further integrate the widely used Faster R-CNN with the mentioned insect pest counter to construct a dual-path network. Extensive experimental simulations show that the two proposed insect pest counters achieve significant improvement in terms of F1 score against the state-of-the-art object detectors as well as insect pest detection methods.
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Lehner, Arthur, and Thomas Blaschke. "A Generic Classification Scheme for Urban Structure Types." Remote Sensing 11, no. 2 (January 17, 2019): 173. http://dx.doi.org/10.3390/rs11020173.
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This paper presents a proposal for a generic urban structure type (UST) scheme. Initially developed in the context of urban ecology, the UST approach is increasingly popular in the remote sensing community. However, there is no consistent and standardized UST framework. Until now, the terms land use and certain USTs are often used and described synonymously, or components of structure and use are intermingled. We suggest a generic nomenclature and a respective UST scheme that can be applied worldwide by stakeholders of different disciplines. Based on the insights of a rigorous literature analysis, we formulate a generic structural- and object-based typology, allowing for the generation of hierarchically and terminologically consistent USTs. The developed terminology exclusively focuses on morphology, urban structures and the general exterior appearance of buildings. It builds on the delimitation of spatial objects at several scales and leaves out all social aspects and land use aspects of an urban area. These underlying objects or urban artefacts and their structure- and object-related features, such as texture, patterns, shape, etc. are the core of the hierarchically structured UST scheme. Finally, the authors present a generic framework for the implementation of a remote sensing-based UST classification along with the requirements regarding sensors, data and data types.
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Song, Shiran, Jianhua Liu, Yuan Liu, Guoqiang Feng, Hui Han, Yuan Yao, and Mingyi Du. "Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery." Sensors 20, no. 2 (January 10, 2020): 397. http://dx.doi.org/10.3390/s20020397.
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High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies. The rich detail information provides better representation of the internal components of each object category and better reflects the relationships between adjacent objects. In this context, recognition methods such as geographic object-based image analysis (GEOBIA) have improved significantly. However, these methods focus mainly on bottom-up classifications from visual features to semantic categories, but ignore top-down feedback which can optimize recognition results. In recent years, deep learning has been applied in the field of remote sensing measurements because of its powerful feature extraction ability. A special convolutional neural network (CNN) based region proposal generation and object detection integrated framework has greatly improved the performance of object detection for HSRRSI, which provides a new method for water body recognition based on remote sensing data. This study uses the excellent “self-learning ability” of deep learning to construct a modified structure of the Mask R-CNN method which integrates bottom-up and top-down processes for water recognition. Compared with traditional methods, our method is completely data-driven without prior knowledge, and it can be regarded as a novel technical procedure for water body recognition in practical engineering application. Experimental results indicate that the method produces accurate recognition results for multi-source and multi-temporal water bodies, and can effectively avoid confusion with shadows and other ground features.
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Zhao, Mengxue, Xiangjiu Che, Hualuo Liu, and Quanle Liu. "Medical Prior Knowledge Guided Automatic Detection of Coronary Arteries Calcified Plaque with Cardiac CT." Electronics 9, no. 12 (December 11, 2020): 2122. http://dx.doi.org/10.3390/electronics9122122.
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Calcified plaque in coronary arteries is one major cause and prediction of future coronary artery disease risk. Therefore, the detection of calcified plaque in coronary arteries is exceptionally significant in clinical for slowing coronary artery disease progression. At present, the Convolutional Neural Network (CNN) is exceedingly popular in natural images’ object detection field. Therefore, CNN in the object detection field of medical images also has a wide range of applications. However, many current calcified plaque detection methods in medical images are based on improving the CNN model algorithm, not on the characteristics of medical images. In response, we propose an automatic calcified plaque detection method in non-contrast-enhanced cardiac CT by adding medical prior knowledge. The training data merging with medical prior knowledge through data augmentation makes the object detection algorithm achieve a better detection result. In terms of algorithm, we employ a deep learning tool knows as Faster R-CNN in our method for locating calcified plaque in coronary arteries. To reduce the generation of redundant anchor boxes, Region Proposal Networks is replaced with guided anchoring. Experimental results show that the proposed method achieved a decent detection performance.
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Sethy, Prabira Kumar, Nalini Kanta Barpanda, Amiya Kumar Rath, and Santi Kumari Behera. "Rice false smut detection based on faster R-CNN." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (September 1, 2020): 1590. http://dx.doi.org/10.11591/ijeecs.v19.i3.pp1590-1595.
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<span lang="EN-IN">Rice false smut is one of the most dangerous diseases in rice at the ripening phase caused by Ustilaginoidea Virens. It is one of the most important grain diseases in rice production worldwide. Its epidemics not only lead to yield loss but also reduce grain quality because of multiple mycotoxins generated by the causative pathogen. The pathogen infects developing spikelets and specifically converts individual grain into rice false smut ball. Rice false smut balls seem to be randomly formed in some grains on a panicle of a plant in the paddy field. In this study, we suggest a novel approach for the detection of rice false smut based on faster R-CNN. The process of faster R-CNN comprises regional proposal generation and object detection. The both tasks are done in same convolutional network. Because of such design it is faster for object detection. The faster R-CNN is able to detect the RFS using rectangular labelling from on-field images. The proposed approach is the initial steps to make a prototype for the automatic detection of RFS.</span>
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MUKHERJEE, DIPTI PRASAD, and NILANJAN RAY. "CONTOUR INTERPOLATION USING LEVEL-SET ANALYSIS." International Journal of Image and Graphics 12, no. 01 (January 2012): 1250004. http://dx.doi.org/10.1142/s0219467812500040.
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We propose a novel approach to generate intermediate contours given a sequence of object contours. The proposal unifies shape features through contour curvature analysis and motion between the contours through optic flow analysis. The major contribution of this work is in integrating this shape and image intensity-based contour interpolation scheme in a level-set framework. The interpolated contours between an initial and a target contour act as missing link and establish a path along which contour deformation has taken place. We have shown that for different application domains such as 3D organ visualization (the generation of contours between two spatially apart contours of 2D slice images of a 3D organ), the meteorological applications of tracing, and the path of a developing cyclone (when satellite images are taken at distant time points and the shape of cyclone in between two consecutive satellite images are of interest), the proposal has outperformed the competing approaches.
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Zhang, Chao, Xuequan Lu, Katsuya Hotta, and Xi Yang. "G2MF-WA: Geometric multi-model fitting with weakly annotated data." Computational Visual Media 6, no. 2 (April 2, 2020): 135–45. http://dx.doi.org/10.1007/s41095-020-0166-8.
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Abstract In this paper we address the problem of geometric multi-model fitting using a few weakly annotated data points, which has been little studied so far. In weak annotating (WA), most manual annotations are supposed to be correct yet inevitably mixed with incorrect ones. SuchWA data can naturally arise through interaction in various tasks. For example, in the case of homography estimation, one can easily annotate points on the same plane or object with a single label by observing the image. Motivated by this, we propose a novel method to make full use of WA data to boost multi-model fitting performance. Specifically, a graph for model proposal sampling is first constructed using the WA data, given the prior that WA data annotated with the same weak label has a high probability of belonging to the same model. By incorporating this prior knowledge into the calculation of edge probabilities, vertices (i.e., data points) lying on or near the latent model are likely to be associated and further form a subset or cluster for effective proposal generation. Having generated proposals, a-expansion is used for labeling, and our method in return updates the proposals. This procedure works in an iterative way. Extensive experiments validate our method and show that it produces noticeably better results than state-of-the-art techniques in most cases.
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Cuccoli, Alessandro, Davide Nuzzi, Ruggero Vaia, and Paola Verrucchi. "Using solitons for manipulating qubits." International Journal of Quantum Information 12, no. 02 (March 2014): 1461013. http://dx.doi.org/10.1142/s0219749914610139.
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Many proposals for quantum devices are based on qubits that are physically realized by the spin magnetic moment of some quantum object. In this case, one of the most often adopted strategies for manipulating qubits is that of using external magnetic fields. However, selectively applying a field just to one qubit may be a practically unattainable goal, as it is, for instance, in most solid-state based setups. In this work, we present a proposal for using nonlinear excitations of solitonic type to accomplish the above task. Our scheme entails the generation of a dynamical soliton in a classical spin-chain which is locally coupled with one qubit: as the soliton runs through, the qubit behaves, due to its interaction with the chain, as if it were subject to a magnetic field with a time dependence that follows from the soliton's features. We here present results for the time evolution of the qubit density-matrix induced by the overall dynamics of the above scheme.
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García-Molina, Diego F., Samuel López-Lago, Rafael E. Hidalgo-Fernandez, and Paula Triviño-Tarradas. "Digitalization and 3D Documentation Techniques Applied to Two Pieces of Visigothic Sculptural Heritage in Merida Through Structured Light Scanning." Journal on Computing and Cultural Heritage 14, no. 4 (December 31, 2021): 1–19. http://dx.doi.org/10.1145/3427381.
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Technological advancements have a great impact on the dissemination and understanding of the cultural heritage reality due to innovative techniques. These innovations are based on high-precision and high-resolution technologies that allow for the geometric documentation of any object within the fields of history and the arts. Through these techniques, new proposals may be studied and objects can be placed in any historical context. Three-dimensional (3D) digitization allows one to obtain a digital 3D model, which can be handled virtually and recreated at any historical period, enabling the conservation and safeguarding of cultural heritage. Society currently demands new visualization techniques that allow interacting with architectural and artistic heritage, which have been applied in numerous virtual reconstructions of historical sites or singular archaeological pieces. This project allowed us to geometrically document a reused piece with two surfaces (shield and columns) and a plaque of the city of Merida using a structured light scanner from a theoretical-practical perspective. The 3D virtual reconstruction of the pieces was accomplished within this study. The generation of QR codes enabled the interactive display of the heritage pieces. Likewise, a proposal was made to reuse the aforementioned pieces through virtual archaeology. The initial hypothesis is based on the possible existence of a Visigothic niche as an original form. This research reports significant advances in the conservation and exploitation of cultural heritage.
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Jiang, Yanting, Jia Yan, Ci’en Fan, Wenxuan Shi, and Dexiang Deng. "An improved real-time object proposals generation method based on local binary pattern." International Journal of Advanced Robotic Systems 14, no. 4 (July 1, 2017): 172988141772467. http://dx.doi.org/10.1177/1729881417724679.
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Generating a group of category-independent proposals of objects in an image within a very short time is an effective approach to accelerate traditional sliding window search, which has been widely used in preprocessing step of object recognition. In this article, we propose a novel object proposals generation method to produce an order set of candidate windows covering most of object instances. With combination of gradient and local binary pattern, our approach achieves better performance than BING in finding occluded objects and objects in dim lighting conditions. In experiments on the challenging PASCAL VOC 2007 data set, we show that our approach is significantly more accurate than BING. In particular, using 2000 proposals, we achieve 97.6% object detection rate and 69.3% mean average best overlap. Moreover, our proposed method is very efficient and takes only about 0.006 s per image on a laptop central processing unit. The detection speed and high accuracy of proposed method mean that it can be applied to recognizing specific objects in robot visions.
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Wang, Xuchu, Fusheng Wang, and Yanmin Niu. "A Convolutional Neural Network Combining Discriminative Dictionary Learning and Sequence Tracking for Left Ventricular Detection." Sensors 21, no. 11 (May 26, 2021): 3693. http://dx.doi.org/10.3390/s21113693.
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Cardiac MRI left ventricular (LV) detection is frequently employed to assist cardiac registration or segmentation in computer-aided diagnosis of heart diseases. Focusing on the challenging problems in LV detection, such as the large span and varying size of LV areas in MRI, as well as the heterogeneous myocardial and blood pool parts in LV areas, a convolutional neural network (CNN) detection method combining discriminative dictionary learning and sequence tracking is proposed in this paper. To efficiently represent the different sub-objects in LV area, the method deploys discriminant dictionary to classify the superpixel oversegmented regions, then the target LV region is constructed by label merging and multi-scale adaptive anchors are generated in the target region for handling the varying sizes. Combining with non-differential anchors in regional proposal network, the left ventricle object is localized by the CNN based regression and classification strategy. In order to solve the problem of slow classification speed of discriminative dictionary, a fast generation module of left ventricular scale adaptive anchors based on sequence tracking is also proposed on the same individual. The method and its variants were tested on the heart atlas data set. Experimental results verified the effectiveness of the proposed method and according to some evaluation indicators, it obtained 92.95% in AP50 metric and it was the most competitive result compared to typical related methods. The combination of discriminative dictionary learning and scale adaptive anchor improves adaptability of the proposed algorithm to the varying left ventricular areas. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement.
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Chang, Ray-I., Chao-Lung Ting, Syuan-Yi Wu, and Peng-Yeng Yin. "Context-Dependent Object Proposal and Recognition." Symmetry 12, no. 10 (September 30, 2020): 1619. http://dx.doi.org/10.3390/sym12101619.
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Accurate and fast object recognition is crucial in applications such as automatic driving and unmanned aerial vehicles. Traditional object recognition methods relying on image-wise computations cannot afford such real-time applications. Object proposal methods appear to fit into this scenario by segmenting object-like regions to be further analyzed by sophisticated recognition models. Traditional object proposal methods have the drawback of generating many proposals in order to maintain a satisfactory recall of true objects. This paper presents two proposal refinement strategies based on low-level cues and context-dependent features, respectively. The low-level cues are used to enhance the edge image, while the context-dependent features are verified to rule out false objects that are irrelevant to our application. In particular, the context of the drink commodity is considered because the drink commodity has the largest sales in Taiwan’s convenience store chains, and the analysis of its context has great value in marketing and management. We further developed a support vector machine (SVM) based on the Bag of Words (BoW) model with scale-invariant feature transform (SIFT) descriptors to recognize the proposals. The experimental results show that our object proposal method generates many fewer proposals than those generated by Selective Search and EdgeBoxes, with similar recall. For the performance of SVM, at least 82% of drink objects are correctly recognized for test datasets of various challenging difficulties.
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Angulo-Fornos, Roque, and Manuel Castellano-Román. "HBIM as Support of Preventive Conservation Actions in Heritage Architecture. Experience of the Renaissance Quadrant Façade of the Cathedral of Seville." Applied Sciences 10, no. 7 (April 2, 2020): 2428. http://dx.doi.org/10.3390/app10072428.
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This paper discusses the generation of Historic Building Information Models (HBIM) for the management of heritage information aimed at the preventive conservation of assets of cultural interest, through its experimentation in a specific case study: the façade of the Renaissance quadrant of the Cathedral of Seville. Two methodological aspects are presented: On the one hand, the process of modeling the solid entities that compose the digital model of the object of study, based on the semi-automatic estimation of the generating surfaces of the main faces; on the other hand, a methodological proposal for the modeling of information on the surface of the model. A series of images and data tables are shown as a result of the application of these methods. These represent the process of introducing information related to the current conservation status documentation and recording the treatments included in the preventive conservation works recently developed by a specialized company. The implementation of the digital model in the exposed work validates it as a solvency option, provided from the infographic medium, when facing the need to contain, manage and visualize all the information generated in preventive conservation actions on heritage architecture, facilitating, in turn, cross-cutting relationships between the different analysis that result in a deeper knowledge of this type of building.
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Tesoriero, Ricardo, Gabriel Sebastian, and Jose A. Gallud. "TagML—An Implementation Specific Model to Generate Tag-Based Documents." Electronics 9, no. 7 (July 5, 2020): 1097. http://dx.doi.org/10.3390/electronics9071097.
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This article describes TagML, a method to generate collections of XML documents using model-to-model (M2M) transformations. To accomplish this goal, we define the TagML meta-model and the TagML-to-XML model-to-text transformation. While TagML models represent the essential characteristics of collections of XML documents, the TagML-to-XML transformation generates the textual representation of collections of XML documents from TagML models. This approach enables developers to define model-to-model transformations to generate TagML models. These models are turned into text applying the TagML-to-XML transformation. Consequently, developers are able to use declarative languages to define model-to-text transformations that generate XML documents, instead of traditional archetype-based languages to define model-to-text transformations that generate collections of XML documents. The TagML model editor as well as the TagML-to-XML transformation were developed as Eclipse plugins using the Eclipse Modeling Framework. The plugin has been developed following the Object Modeling Group standards to ensure the compatibility with legacy tools. Using TagML, unlike other previous proposals, implies the use of model-to-model transformations to generate XML documents, instead of model-to-text transformations, which results on an improvement of the transformation readability and reliability, as well as a reduction of the transformation maintenance costs. The proposed approach helps developers to define transformations less prone to errors than using the traditional approach. The novelty of this approach is based on the way XML documents are generated using model-to-model transformations instead of traditional model-to-text transformations. Moreover, the simplicity of the proposed approach enables the generation of XML documents without the need for any transformation configuration, which does not penalize the model reuse. To illustrate the features of the proposal, we present the generation of XHTML documents using UML class diagrams as input models. The evaluation section demonstrates that the proposed method is less prone to errors than the traditional one.
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Li, Jin, Daifu Yan, Kuan Luan, Zeyu Li, and Hong Liang. "Deep Learning-Based Bird’s Nest Detection on Transmission Lines Using UAV Imagery." Applied Sciences 10, no. 18 (September 4, 2020): 6147. http://dx.doi.org/10.3390/app10186147.
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In order to ensure the safety of transmission lines, the use of unmanned aerial vehicle (UAV) images for automatic object detection has important application prospects, such as the detection of birds’ nests. The traditional bird’s nest detection methods mainly include the study of morphological characteristics of the bird’s nest. These methods have poor applicability and low accuracy. In this work, we propose a deep learning-based birds’ nests automatic detection framework—region of interest (ROI) mining faster region-based convolutional neural networks (RCNN). First, the prior dimensions of anchors are obtained by using k-means clustering to improve the accuracy of coordinate boxes generation. Second, in order to balance the number of foreground and background samples in the training process, the focal loss function is introduced in the region proposal network (RPN) classification stage. Finally, the ROI mining module is added to solve the class imbalance problem in the classification stage, combined with the characteristics of difficult-to-classify bird’s nest samples in the UAV images. After parameter optimization and experimental verification, the deep learning-based bird’s nest automatic detection framework proposed in this work achieves high detection accuracy. In addition, the mean average precision (mAP) and formula 1 (F1) score of the proposed method are higher than the original faster RCNN and cascade RCNN. Our comparative analysis verifies the effectiveness of the proposed method.
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Okahana, Yuuhi, and Yusuke Gotoh. "A Parallelizing Method for Generation of Voronoi Diagram Using Contact Zone." Journal of Data Intelligence 1, no. 2 (June 2020): 159–75. http://dx.doi.org/10.26421/jdi1.2-4.
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Due to the recent popularization of the Geographic Information System (GIS), spatial network environments that can display the changes of spatial axes on mobile devices are receiving great attention. In spatial network environments, since a query object that seeks location information selects several candidate target objects based on the search conditions, we often use a k-nearest neighbor (kNN) search, which seeks several target objects near the query object. However, since a kNN search needs to find the kNN by calculating the distance from the query to all the objects, the computational complexity might become too large based on the number of objects. To reduce this computation time in a kNN search, many researchers have proposed a search method that divides regions using a Voronoi diagram. However, since conventional methods generate Voronoi diagrams for objects in order, the processing time for generating Voronoi diagrams might become too large when the number of objects is increased. In this paper, we propose a generation method of the Voronoi diagram by parallelizing the generation of Voronoi regions using a contact zone. Our proposed method can reduce the processing time of generating the Voronoi diagram by generating Voronoi regions in parallel based on the number of targets. Our evaluation confirmed that the processing time under the proposed method was reduced about 15.9\% more than conventional methods that are not parallelized.
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Chang, Chuan-Yu, Kathiravan Srinivasan, Wei-Chun Wang, Ganapathy Pattukandan Ganapathy, Durai Raj Vincent, and N. Deepa. "Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets." Electronics 9, no. 1 (December 28, 2019): 45. http://dx.doi.org/10.3390/electronics9010045.
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In recent times, the application of enabling technologies such as digital shearography combined with deep learning approaches in the smart quality assessment of tires, which leads to intelligent tire manufacturing practices with automated defects detection. Digital shearography is a prominent approach that can be employed for identifying the defects in tires, usually not visible to human eyes. In this research, the bubble defects in tire shearography images are detected using a unique ensemble hybrid amalgamation of the convolutional neural networks/ConvNets with high-performance Faster Region-based convolutional neural networks. It can be noticed that the routine of region-proposal generation along with object detection is accomplished using the ConvNets. Primarily, the sliding window based ConvNets are utilized in the proposed model for dividing the input shearography images into regions, in order to identify the bubble defects. Subsequently, this is followed by implementing the Faster Region-based ConvNets for identifying the bubble defects in the tire shearography images and further, it also helps to minimize the false-positive ratio (sometimes referred to as the false alarm ratio). Moreover, it is evident from the experimental results that the proposed hybrid model offers a cent percent detection of bubble defects in the tire shearography images. Also, it can be witnessed that the false-positive ratio gets minimized to 18 percent.
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Rickel, A. M. "Generation as a social-psychological research object: playing at home or an away match?" Social Psychology and Society 10, no. 2 (2019): 9–18. http://dx.doi.org/10.17759/sps.2019100202.
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The article examines the methodological difficulties associated with studying of the generation category in social psychology. The history of studying the problems of generations and intergenerational conflicts in psychology and humanitarian disciplines is briefly analyzed, the conclusion is drawn about the vastness of both the theoretical concept and the blurriness of the methods of the empirical operationalization of the generation. Theoretically close constructs are given, allowing one to consider generational problems within the framework of other conceptual frameworks. Existing models and theories of generations, including popular science, are considered, their critical analysis is carried out. Some theoretical decisions are proposed that allow one to reduce the problems associated with the operationalization and study of the generation in social psychology.