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Journal articles on the topic 'Mean intersection over union'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Li, Wei, Xueyan Zhu, Xiaochun Wang, et al. "Segmentation and accurate identification of large carious lesions on high quality x-ray images based on Attentional U-Net model. A proof of concept study." Journal of Applied Physics 132, no. 3 (2022): 033103. http://dx.doi.org/10.1063/5.0084593.

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Dental caries is a bacterial infectious disease that destroys the structure of teeth. It is one of the main diseases that endanger human health [R. H. Selwitz, A. I. Ismail, and N. B. Pitts, Lancet 369(9555), 51–59 (2007)]. At present, dentists use both visual exams and radiographs for the detection of caries. Affected by the patient's dental health and the degree of caries demineralization, it is sometimes difficult to accurately identify some dental caries in x-ray images with the naked eye. Therefore, dentists need an intelligent and accurate dental caries recognition system to assist diagn

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Kotlyar, D. I., and A. N. Lomanov. "SEGMENTATION OF PICTURES CONTAINING BLADE EDGE OF A GAS TURBINE ENGINE." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 227 (May 2023): 3–10. http://dx.doi.org/10.14489/vkit.2023.05.pp.003-010.

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The article describes common techniques for semantic segmentation pictures containing edges of gas turbine engines blades for detecting left and right borders for further using in forming trajectory algorithms with direct metal deposition. For analysis such metrics, as pixel accuracy, mean pixel accuracy, intersection over union, frequency weighed intersection over union are used. Classic method of computer vision with threshold filters, border segmentation neural network method, fully convoluted neural network for semantic segmentation are focused on. The classic method of computer vision pro

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Huang, Yijie, Huimin Ouyang, and Xiaodong Miao. "LSOD-YOLOv8: Enhancing YOLOv8n with New Detection Head and Lightweight Module for Efficient Cigarette Detection." Applied Sciences 15, no. 7 (2025): 3961. https://doi.org/10.3390/app15073961.

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Cigarette detection is a crucial component of public safety management. However, detecting such small objects poses significant challenges due to their size and limited feature points. To enhance the accuracy of small target detection, we propose a novel small object detection model, LSOD-YOLOv8 (Lightweight Small Object Detection using YOLOv8). First, we introduce a lightweight adaptive weight downsampling module in the backbone layer of YOLOv8 (You Only Look Once version 8), which not only mitigates information loss caused by conventional convolutions but also reduces the overall parameter c

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Ji, Hengyi, Jionghua Yu, Fengdan Lao, Yanrong Zhuang, Yanbin Wen, and Guanghui Teng. "Automatic Position Detection and Posture Recognition of Grouped Pigs Based on Deep Learning." Agriculture 12, no. 9 (2022): 1314. http://dx.doi.org/10.3390/agriculture12091314.

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The accurate and rapid detection of objects in videos facilitates the identification of abnormal behaviors in pigs and the introduction of preventive measures to reduce morbidity. In addition, accurate and effective pig detection algorithms provide a basis for pig behavior analysis and management decision-making. Monitoring the posture of pigs can enable the detection of the precursors of pig diseases in a timely manner and identify factors that impact pigs’ health, which helps to evaluate their health status and comfort. Excessive sitting represents abnormal behavior when pigs are frustrated

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Borgli, Hanna, Håkon Kvale Stensland, and Pål Halvorsen. "Automatic Prompt Generation Using Class Activation Maps for Foundational Models: A Polyp Segmentation Case Study." Machine Learning and Knowledge Extraction 7, no. 1 (2025): 22. https://doi.org/10.3390/make7010022.

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We introduce a weakly supervised segmentation approach that leverages class activation maps and the Segment Anything Model to generate high-quality masks using only classification data. A pre-trained classifier produces class activation maps that, once thresholded, yield bounding boxes encapsulating the regions of interest. These boxes prompt the SAM to generate detailed segmentation masks, which are then refined by selecting the best overlap with automatically generated masks from the foundational model using the intersection over union metric. In a polyp segmentation case study, our approach

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Bulatov, D., B. Kottler, E. Strauss, et al. "ASSESSING GEO-TYPICAL TECHNIQUES FOR MODELING BUILDINGS USING THERMAL SIMULATIONS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2022 (May 18, 2022): 251–58. http://dx.doi.org/10.5194/isprs-annals-v-4-2022-251-2022.

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Abstract. Building modeling from remote sensing data is essential for creating accurate 3D and 4D digital twins, especially for temperature modeling. In order to represent buildings as gap-free, visually appealing, and rich in details models, geo-typical prototypes should be represented in the scene. The sensor data and freely available OSM data are supposed to provide guidelines for best-possible matching. In this paper, the default similarity function based on intersection over union is extended by terms reflecting the similarity of elevation values, orientation towards the road, and trees i

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Guo, Baoyun, Xiaokai Sun, Cailin Li, Na Sun, Yue Wang, and Yukai Yao. "Semantic Segmentation of Point Cloud Scene via Multi-Scale Feature Aggregation and Adaptive Fusion." Photogrammetric Engineering & Remote Sensing 90, no. 9 (2024): 553–63. http://dx.doi.org/10.14358/pers.23-00076r2.

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Point cloud semantic segmentation is a key step in 3D scene understanding and analysis. In recent years, deep learning–based point cloud semantic segmentation methods have received extensive attention from researchers. Multi-scale neighborhood feature learning methods are suitable for inhomogeneous density point clouds, but different scale branching feature learning increases the computational complexity and makes it difficult to accurately fuse different scale features to express local information. In this study, a point cloud semantic segmentation network based on RandLA-Net with multi-scale

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Qin, Xuan, and Xueyan Yang. "Dielectrophoresis-Assisted 3D LC-Oscillator Array in Complementary Metal Oxide Semiconductor Image Senser for Label-Free and Damage Detection of Ancient Building." Journal of Nanoelectronics and Optoelectronics 18, no. 5 (2023): 604–10. http://dx.doi.org/10.1166/jno.2023.3431.

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In order to improve the efficiency and accuracy of damage detection of ancient buildings, a dielectrophoresis-assisted 3D LC-oscillator array in CMOS image senser for label-free and damage detection of ancient building is proposed to identify damage areas and achieve pixel-level semantic segmentation. The Grid-Deeplab model is used to model the sub-regions of the damaged image with different importance features. The model has the ability to distinguish the effective area of the image, thereby significantly improve the efficiency and accuracy of the damage detection model. Using the mean inters

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Ansari, R. A., and W. Thomas. "CURVELET BASED U-NET FRAMEWORK FOR BUILDING FOOTPRINT IDENTIFICATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W3-2023 (May 12, 2023): 15–19. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-w3-2023-15-2023.

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Abstract. This paper proposes a multiresolution based U-net composite architecture for segmentation of remotely sensed images for building footprint identification. The features derived from curvelet decompositions at different scales are augmented to capture curvilinear discontinuities of the building footprint. This increases the contextual overview of the network as the same data on multiple scales is available for feature extraction and learning. This work further analyses the effects of different multiresolution methods on wavelets and curvelets for decomposition on segmentation performan

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Naudé, August J., and Herman C. Myburgh. "Unification of Road Scene Segmentation Strategies Using Multistream Data and Latent Space Attention." Sensors 23, no. 17 (2023): 7355. http://dx.doi.org/10.3390/s23177355.

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Road scene understanding, as a field of research, has attracted increasing attention in recent years. The development of road scene understanding capabilities that are applicable to real-world road scenarios has seen numerous complications. This has largely been due to the cost and complexity of achieving human-level scene understanding, at which successful segmentation of road scene elements can be achieved with a mean intersection over union score close to 1.0. There is a need for more of a unified approach to road scene segmentation for use in self-driving systems. Previous works have demon

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Wang, Wei, Yuxi Kang, Guanqun Liu, and Xin Wang. "SCU-Net: Semantic Segmentation Network for Learning Channel Information on Remote Sensing Images." Computational Intelligence and Neuroscience 2022 (April 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/8469415.

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Extracting detailed information from remote sensing images is an important direction in semantic segmentation. Not only the amounts of parameters and calculations of the network model in the learning process but also the prediction effect after learning must be considered. This paper designs a new module, the upsampling convolution-deconvolution module (CDeConv). On the basis of CDeConv, a convolutional neural network (CNN) with a channel attention mechanism for semantic segmentation is proposed as a channel upsampling network (SCU-Net). SCU-Net has been verified by experiments. The mean inter

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Chen, Zhaoyi, Ruhui Wu, Yiyan Lin, et al. "Plant Disease Recognition Model Based on Improved YOLOv5." Agronomy 12, no. 2 (2022): 365. http://dx.doi.org/10.3390/agronomy12020365.

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To accurately recognize plant diseases under complex natural conditions, an improved plant disease-recognition model based on the original YOLOv5 network model was established. First, a new InvolutionBottleneck module was used to reduce the numbers of parameters and calculations, and to capture long-distance information in the space. Second, an SE module was added to improve the sensitivity of the model to channel features. Finally, the loss function ‘Generalized Intersection over Union’ was changed to ‘Efficient Intersection over Union’ to address the former’s degeneration into ‘Intersection

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Sussi, Sussi, Husni Emir, Siburian Arthur, Yusuf Rahadian, Budi Harto Agung, and Suwardhi Deni. "Effect of dataset distribution on automatic road extraction in very high-resolution orthophoto using DeepLab V3+." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1650–57. https://doi.org/10.11591/ijai.v13.i2.pp1650-1657.

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Road extraction is one of the stages in the map-making process, which has been done manually, takes a long time, and costs a lot. Deep Learning is used to speed up the road extraction process by performing binary semantic segmentation on the image. We propose DeepLab V3+ to produce road extraction from very high-resolution orthophoto for Indonesia study area, which poses many challenges, such as road obstruction by trees, clouds, building shadows, dense traffic, and similarities to rivers and rice fields. We compared the distribution of datasets to obtain the optimal performance of the DeepLab

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Guo, Rui, Xiaopeng Zhao, Guanzhong Zuo, Ying Wang, and Yi Liang. "Polarimetric Synthetic Aperture Radar Image Semantic Segmentation Network with Lovász-Softmax Loss Optimization." Remote Sensing 15, no. 19 (2023): 4802. http://dx.doi.org/10.3390/rs15194802.

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The deep learning technique has already been successfully applied in the field of microwave remote sensing. Especially, convolutional neural networks have demonstrated remarkable effectiveness in synthetic aperture radar (SAR) image semantic segmentation. In this paper, a Lovász-softmax loss optimization SAR net (LoSARNet) is proposed which optimizes the semantic segmentation metric intersection over union (IOU) instead of using the traditional cross-entropy loss. Meanwhile, making use of the advantages of the dual-path structure, the network extracts feature through the spatial path (SP) and

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Yao, Jiayan, Qianwei Yu, Guangkun Deng, et al. "A Fast and Accurate Obstacle Segmentation Network for Guava-Harvesting Robot via Exploiting Multi-Level Features." Sustainability 14, no. 19 (2022): 12899. http://dx.doi.org/10.3390/su141912899.

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Guava fruit is readily concealed by branches, making it difficult for picking robots to rapidly grip. For the robots to plan collision-free paths, it is crucial to segment branches and fruits. This study investigates a fast and accurate obstacle segmentation network for guava-harvesting robots. At first, to extract feature maps of different levels quickly, Mobilenetv2 is used as a backbone. Afterwards, a feature enhancement module is proposed to fuse multi-level features and recalibrate their channels. On the basis of this, a decoder module is developed, which strengthens the connection betwee

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Nuradili, Pakezhamu, Ji Zhou, Guiyun Zhou, and Farid Melgani. "Deep Learning Method for Wetland Segmentation in Unmanned Aerial Vehicle Multispectral Imagery." Remote Sensing 16, no. 24 (2024): 4777. https://doi.org/10.3390/rs16244777.

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This study highlights the importance of unmanned aerial vehicle (UAV) multispectral (MS) imagery for the accurate delineation and analysis of wetland ecosystems, which is crucial for their conservation and management. We present an enhanced semantic segmentation algorithm designed for UAV MS imagery, which incorporates thermal infrared (TIR) data to improve segmentation outcomes. Our approach, involving meticulous image preprocessing, customized network architecture, and iterative training procedures, aims to refine wetland boundary delineation. The algorithm demonstrates strong segmentation r

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Deng, Hanbing, Tongyu Xu, Yuncheng Zhou, and Teng Miao. "Depth Density Achieves a Better Result for Semantic Segmentation with the Kinect System." Sensors 20, no. 3 (2020): 812. http://dx.doi.org/10.3390/s20030812.

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Image segmentation is one of the most important methods for animal phenome research. Since the advent of deep learning, many researchers have looked at multilayer convolutional neural networks to solve the problems of image segmentation. A network simplifies the task of image segmentation with automatic feature extraction. Many networks struggle to output accurate details when dealing with pixel-level segmentation. In this paper, we propose a new concept: Depth density. Based on a depth image, produced by a Kinect system, we design a new function to calculate the depth density value of each pi

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Juyal, Amit. "A Deep Learning-Based Approach for Real-Time Object Detection and Recognition." Mathematical Statistician and Engineering Applications 70, no. 2 (2021): 1304–14. http://dx.doi.org/10.17762/msea.v70i2.2322.

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Object detection and recognition is an essential task in computer vision with numerous real-world applications such as surveillance, self-driving cars, and robotics. In recent years, deep learning-based approaches have significantly improved the accuracy and speed of object detection and recognition. The You Only Look Once version 3 (YOLOv3) algorithm is a popular deep learning-based approach that can detect and recognize objects in real-time. The Common Objects in Context (COCO) dataset is a large-scale dataset with over 330,000 labeled images and more than 2.5 million object instances, makin

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Chen, Junjie, Qian Su, Yunbin Niu, Zongyu Zhang, and Jinghao Liu. "A Handheld LiDAR-Based Semantic Automatic Segmentation Method for Complex Railroad Line Model Reconstruction." Remote Sensing 15, no. 18 (2023): 4504. http://dx.doi.org/10.3390/rs15184504.

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To ensure efficient railroad operation and maintenance management, the accurate reconstruction of railroad BIM models is a crucial step. This paper proposes a workflow for automated segmentation and reconstruction of railroad structures using point cloud data, without relying on intensity or trajectory information. The workflow consists of four main components: point cloud adaptive denoising, scene segmentation, structure segmentation combined with deep learning, and model reconstruction. The proposed workflow was validated using two datasets with significant differences in railroad line point

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Krapf, Sebastian, Lukas Bogenrieder, Fabian Netzler, Georg Balke, and Markus Lienkamp. "RID—Roof Information Dataset for Computer Vision-Based Photovoltaic Potential Assessment." Remote Sensing 14, no. 10 (2022): 2299. http://dx.doi.org/10.3390/rs14102299.

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Computer vision has great potential to accelerate the global scale of photovoltaic potential analysis by extracting detailed roof information from high-resolution aerial images, but the lack of existing deep learning datasets is a major barrier. Therefore, we present the Roof Information Dataset for semantic segmentation of roof segments and roof superstructures. We assessed the label quality of initial roof superstructure annotations by conducting an annotation experiment and identified annotator agreements of 0.15–0.70 mean intersection over union, depending on the class. We discuss associat

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Xie, Jiaxing, Tingwei Jing, Binhan Chen, et al. "Method for Segmentation of Litchi Branches Based on the Improved DeepLabv3+." Agronomy 12, no. 11 (2022): 2812. http://dx.doi.org/10.3390/agronomy12112812.

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It is necessary to develop automatic picking technology to improve the efficiency of litchi picking, and the accurate segmentation of litchi branches is the key that allows robots to complete the picking task. To solve the problem of inaccurate segmentation of litchi branches under natural conditions, this paper proposes a segmentation method for litchi branches based on the improved DeepLabv3+, which replaced the backbone network of DeepLabv3+ and used the Dilated Residual Networks as the backbone network to enhance the model’s feature extraction capability. During the training process, a com

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Jia, Xuejun, Xiaoxiong Zhou, Chunyi Su, et al. "High-Precision and Lightweight Model for Rapid Safety Helmet Detection." Sensors 24, no. 21 (2024): 6985. http://dx.doi.org/10.3390/s24216985.

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This paper presents significant improvements in the accuracy and computational efficiency of safety helmet detection within industrial environments through the optimization of the you only look once version 5 small (YOLOv5s) model structure and the enhancement of its loss function. We introduce the convolutional block attention module (CBAM) to bolster the model’s sensitivity to key features, thereby enhancing detection accuracy. To address potential performance degradation issues associated with the complete intersection over union (CIoU) loss function in the original model, we implement the

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Lei, Lin, Ruifeng Duan, Feng Yang, and Longhang Xu. "Low Complexity Forest Fire Detection Based on Improved YOLOv8 Network." Forests 15, no. 9 (2024): 1652. http://dx.doi.org/10.3390/f15091652.

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Forest fires pose a significant threat to ecosystems and communities. This study introduces innovative enhancements to the YOLOv8n object detection algorithm, significantly improving its efficiency and accuracy for real-time forest fire monitoring. By employing Depthwise Separable Convolution and Ghost Convolution, the model’s computational complexity is significantly reduced, making it suitable for deployment on resource-constrained edge devices. Additionally, Dynamic UpSampling and Coordinate Attention mechanisms enhance the model’s ability to capture multi-scale features and focus on releva

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Senousi, Ahmad M., Wael Ahmed, Xintao Liu, and Walid Darwish. "Automated Digitization Approach for Road Intersections Mapping: Leveraging Azimuth and Curve Detection from Geo-Spatial Data." ISPRS International Journal of Geo-Information 14, no. 7 (2025): 264. https://doi.org/10.3390/ijgi14070264.

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Effective maintenance and management of road infrastructure are essential for community well-being, economic stability, and cost efficiency. Well-maintained roads reduce accident risks, improve safety, shorten travel times, lower vehicle repair costs, and facilitate the flow of goods, all of which positively contribute to GDP and economic development. Accurate intersection mapping forms the foundation of effective road asset management, yet traditional manual digitization methods remain time-consuming and prone to gaps and overlaps. This study presents an automated computational geometry solut

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Li, Jing, Yingqian Chen, Weiye Li, and Jinan Gu. "Balanced-YOLOv3: Addressing the Imbalance Problem of Object Detection in PCB Assembly Scene." Electronics 11, no. 8 (2022): 1183. http://dx.doi.org/10.3390/electronics11081183.

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The object detection algorithm of the PCB (Printed Circuit Board) assembly scene based on CNN (Convolutional Neural Network) can significantly improve the production capacity of intelligent manufacturing of electronic products. However, the object class imbalance in the PCB assembly scene, the multi-scale feature imbalance, and the positive/negative sample imbalance in the CNN have become critical problems restricting object detection performance. Based on YOLOv3, this paper proposes a class-balanced Train/Val (Training set/Validation set) split method for object class imbalance, an additional

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Li, Long, Zhiyuan Liu, Hengyi Zhao, Lin Xue, and Jianbo Wu. "The Bearing Surface Defect Detection Method Combining Magnetic Particle Testing and Deep Learning." Applied Sciences 14, no. 5 (2024): 1747. http://dx.doi.org/10.3390/app14051747.

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As a critical foundational component, bearings find widespread application in various mechanical equipment. In order to achieve automated defect detection in the bearing-manufacturing process, a defect detection algorithm combining magnetic particle inspection with deep learning is proposed. Dynamic thresholding and generative adversarial network (GAN) methods are employed to extract defect samples from bearing images and augment the dataset, thereby enhancing data diversity. To mitigate the impact of irrelevant displays in bearing images, a coordinated attention (CA) mechanism is introduced i

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Zhu, Wanling, and Yuan Jia. "High-resolution remote sensing image land cover classification based on EAR-HRNetV2." Journal of Physics: Conference Series 2593, no. 1 (2023): 012002. http://dx.doi.org/10.1088/1742-6596/2593/1/012002.

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Abstract The application of land cover classification is very extensive, it can be used in land resources management, urban planning, agricultural and other fields. The multi-scale variation of targets in high-resolution remote sensing images leads to low accuracy in land cover classification tasks. In response to this problem, an improved semantic segmentation model named EAR-HRNetV2 is proposed based on HRNetV2. Firstly, the attention mechanism is added to the Bottleneck residual and Basic residual modules to obtain more efficient features. Then, the atrous spatial pyramid pooling is added t

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Allen, Timothy J., Leah C. Henze Bancroft, Kang Wang, et al. "Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network." Tomography 9, no. 3 (2023): 967–80. http://dx.doi.org/10.3390/tomography9030079.

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Graphically prescribed patient-specific imaging volumes and local pre-scan volumes are routinely placed by MRI technologists to optimize image quality. However, manual placement of these volumes by MR technologists is time-consuming, tedious, and subject to intra- and inter-operator variability. Resolving these bottlenecks is critical with the rise in abbreviated breast MRI exams for screening purposes. This work proposes an automated approach for the placement of scan and pre-scan volumes for breast MRI. Anatomic 3-plane scout image series and associated scan volumes were retrospectively coll

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Jiang, Yiwen. "Surface defect detection of steel based on improved YOLOv5 algorithm." Mathematical Biosciences and Engineering 20, no. 11 (2023): 19858–70. http://dx.doi.org/10.3934/mbe.2023879.

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<abstract> <p>To address the challenge of achieving a balance between efficiency and performance in steel surface defect detection, this paper presents a novel algorithm that enhances the YOLOv5 defect detection model. The enhancement process begins by employing the <italic>K-means</italic>++ algorithm to fine-tune the location of the prior anchor boxes, improving the matching process. Subsequently, the loss function is transitioned from generalized intersection over union (GIOU) to efficient intersection over union (EIOU) to mitigate the former's degeneration issues. T

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Budiarsa, Rahmat, Retantyo Wardoyo, and Aina Musdholifah. "Face recognition with occluded face using improve intersection over union of region proposal network on Mask region convolutional neural network." Face recognition with occluded face using improve intersection over union of region proposal network on Mask region convolutional neural network 14, no. 3 (2024): 3256–65. https://doi.org/10.11591/ijece.v14i3.pp3256-3265.

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Face recognition entails detecting and identifying facial attributes. Mask region convolutional neural network (R-CNN) method is a prominent approach, while prior research predominantly delved into refining loss functions and perfecting object and face detection, recognizing, and identifying faces using imperfect data remained relatively unexplored. This study focuses on an occluded dataset comprising Indonesian faces, wherein 'occluded' denotes facial data that lacks complete visibility-encompassing instances where objects obscure faces or are partially cropped.

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Qiu, Shi, Xianhua Liu, Jun Peng, et al. "Fine-Grained Point Cloud Semantic Segmentation of Complex Railway Bridge Scenes from UAVs Using Improved DGCNN." Structural Control and Health Monitoring 2023 (October 3, 2023): 1–17. http://dx.doi.org/10.1155/2023/3733799.

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Automatic semantic segmentation of point clouds in railway bridge scenes is a crucial step in the digitization process and is required for a variety of subapplications including digital twin reconstruction and component geometric quality verification. This paper details a method for reliably and effectively segmenting point clouds acquired from complex railway bridge scenes by unmanned aerial vehicles (UAVs). The method involves segmenting seven common infrastructure elements in railway bridge point clouds using an improved DGCNN after processing low-quality point clouds from UAVs with a score

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Niu, Shengsuo, Xiaosen Zhou, Dasen Zhou, Zhiyao Yang, Haiping Liang, and Haifeng Su. "Fault Detection in Power Distribution Networks Based on Comprehensive-YOLOv5." Sensors 23, no. 14 (2023): 6410. http://dx.doi.org/10.3390/s23146410.

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Real-time fault detection in power distribution networks has become a popular issue in current power systems. However, the low power and computational capabilities of edge devices often fail to meet the requirements of real-time detection. To overcome these challenges, this paper proposes a lightweight algorithm, named Comprehensive-YOLOv5, for identifying defects in distribution networks. The proposed method focuses on achieving rapid localization and accurate identification of three common defects: insulator without loop, cable detachment from the insulator, and cable detachment from the spa

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Peng, Yan, Zhaoming Zhang, Guojin He, and Mingyue Wei. "An Improved GrabCut Method Based on a Visual Attention Model for Rare-Earth Ore Mining Area Recognition with High-Resolution Remote Sensing Images." Remote Sensing 11, no. 8 (2019): 987. http://dx.doi.org/10.3390/rs11080987.

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An improved GrabCut method based on a visual attention model is proposed to extract rare-earth ore mining area information using high-resolution remote sensing images. The proposed method makes use of advantages of both the visual attention model and GrabCut method, and the visual attention model was referenced to generate a saliency map as the initial of the GrabCut method instead of manual initialization. Normalized Difference Vegetation Index (NDVI) was designed as a bound term added into the Energy Function of GrabCut to further improve the accuracy of the segmentation result. The proposed

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Zhang, Yan, Steffen Müller, Benedict Stephan, Horst-Michael Gross, and Gunther Notni. "Point Cloud Hand–Object Segmentation Using Multimodal Imaging with Thermal and Color Data for Safe Robotic Object Handover." Sensors 21, no. 16 (2021): 5676. http://dx.doi.org/10.3390/s21165676.

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This paper presents an application of neural networks operating on multimodal 3D data (3D point cloud, RGB, thermal) to effectively and precisely segment human hands and objects held in hand to realize a safe human–robot object handover. We discuss the problems encountered in building a multimodal sensor system, while the focus is on the calibration and alignment of a set of cameras including RGB, thermal, and NIR cameras. We propose the use of a copper–plastic chessboard calibration target with an internal active light source (near-infrared and visible light). By brief heating, the calibratio

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Lv, Ning, Jian Xiao, and Yujing Qiao. "Object Detection Algorithm for Surface Defects Based on a Novel YOLOv3 Model." Processes 10, no. 4 (2022): 701. http://dx.doi.org/10.3390/pr10040701.

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The surface defects of industrial structural parts have the characteristics of a large-scale span and many small objects, so a novel YOLOv3 model, the YOLOv3-ALL algorithm, is proposed in this paper to solve the problem of precise defect detection. The K-means++ algorithm is combined with the intersection-over-union (IoU) and comparison of the prior box for clustering, which improves the clustering effect. The convolutional block attention module (CBAM) is embedded in the network, thus improving the ability of the network to obtain key information in the image. By adding fourth-scale predictio

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Yi, Feifan, Ahmad Sufril Azlan Mohamed, Mohd Halim Mohd Noor, Fakhrozi Che Ani, and Zol Effendi Zolkefli. "YOLOv8-DEE: a high-precision model for printed circuit board defect detection." PeerJ Computer Science 10 (December 12, 2024): e2548. https://doi.org/10.7717/peerj-cs.2548.

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Defects in printed circuit boards (PCBs) occurring during the production process of consumer electronic products can have a substantial impact on product quality, compromising both stability and reliability. Despite considerable efforts in PCB defect inspection, current detection models struggle with accuracy due to complex backgrounds and multi-scale characteristics of PCB defects. This article introduces a novel network, YOLOv8-DSC-EMA-EIoU (YOLOv8-DEE), to address these challenges by enhancing the YOLOv8-L model. Firstly, an improved backbone network incorporating depthwise separable convol

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Zhao, Lulu, Yanan Zhao, Ting Liu, and Hanbing Deng. "A Weakly Supervised Semantic Segmentation Model of Maize Seedlings and Weed Images Based on Scrawl Labels." Sensors 23, no. 24 (2023): 9846. http://dx.doi.org/10.3390/s23249846.

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The task of semantic segmentation of maize and weed images using fully supervised deep learning models requires a large number of pixel-level mask labels, and the complex morphology of the maize and weeds themselves can further increase the cost of image annotation. To solve this problem, we proposed a Scrawl Label-based Weakly Supervised Semantic Segmentation Network (SL-Net). SL-Net consists of a pseudo label generation module, encoder, and decoder. The pseudo label generation module converts scrawl labels into pseudo labels that replace manual labels that are involved in network training, i

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Yu, Dexin, Zimin Yuan, Xincheng Wu, Yipen Wang, and Xiaojia Liu. "Real-Time Monitoring Method for Traffic Surveillance Scenarios Based on Enhanced YOLOv7." Applied Sciences 14, no. 16 (2024): 7383. http://dx.doi.org/10.3390/app14167383.

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Due to the impact of scale variation of vehicle targets and changes in traffic environments in large-scale traffic monitoring systems, vehicle target detection methods often face challenges. To improve the adaptability of detection methods to these variations, we proposed an enhanced YOLOv7 for traffic systems (ETS-YOLOv7). To mitigate the effects of complex environments, we introduced the convolutional block attention module (CBAM) into the YOLOv7 framework, which filters important features in both channel and spatial dimensions, thereby enhancing the model’s capability to recognize traffic o

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Katayama, Takafumi, Tian Song, Xiantao Jiang, Jenq-Shiou Leu, and Takashi Shimamoto. "Domain Adaptation through Photorealistic Enhanced Images for Semantic Segmentation." Mathematical Problems in Engineering 2022 (July 15, 2022): 1–8. http://dx.doi.org/10.1155/2022/1848857.

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In this paper, three types of domain adaptation which are defined as image-level domain adaptation, interdomain adaptation, and intradomain adaptation are efficiently combined to construct a high efficiency framework for semantic segmentation. The proposed domain adaptation platform can achieve a high reduction of time-consuming to generate exhausted supervised data in the real world using photorealistic images. The proposed framework achieved a mean Intersection-over-Union (mIoU) of 45.0%. Furthermore, by combining the proposed method with intradomain adaptation, the improvement of 1.2% mIoU

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Adwan, Mostafa M. H., and Ihab Elaff Ihab Elaff. "Glioma Segmentation on 3D MRI Using 2D U-Net: A Study on the BraTS Dataset." International Journal of Advances in Engineering and Management 7, no. 7 (2025): 895–902. https://doi.org/10.35629/5252-0707895902.

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Gliomas are highly aggressive brain tumors, and their precise segmentation in MRI scans is important for treatment planning. In this study, we employ a 2D U-Net model for automatic segmentation of brain tumors using the BraTS dataset. Our technique segments sub-regions such as the enhancing tumor, tumor core, and entire tumor from four MRI sequences (T1, T1CE, T2, FLAIR). The best-performing model achieved a mean Intersection over Union (IoU) of 81% and a Dice score of 65.5%, showing the viability of 2D U-Net for real-world neuroimaging applications.

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Li, Muqing, Ziyi Zhu, Ruilin Xu, Yinqiu Feng, and Lingxi Xiao. "Research on Image Classification And Semantic Segmentation Model Based on Convolutional Neural Network." Journal of Computing and Electronic Information Management 12, no. 3 (2024): 94–100. http://dx.doi.org/10.54097/qg7hakzu.

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This paper investigates convolutional neural network (CNN)-based approaches for image classification and semantic segmentation, with a focus on addressing spatial detail loss and multi-scale feature integration issues prevalent in semantic segmentation. The introduced EDNET model tackles these challenges through the incorporation of spatial information branches and the design of efficient feature fusion mechanisms. It further enhances performance via the use of global pooling and boundary refinement modules. Evaluations on the PASCAL VOC 2012 dataset reveal an 11.67% increase in mean intersect

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Budiarsa, Rahmat, Retantyo Wardoyo, and Aina Musdholifah. "Face recognition with occluded face using improve intersection over union of region proposal network on Mask region convolutional neural network." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (2024): 3256. http://dx.doi.org/10.11591/ijece.v14i3.pp3256-3265.

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Face recognition entails detecting and identifying facial attributes. Mask region convolutional neural network (R-CNN) method is a prominent approach, while prior research predominantly delved into refining loss functions and perfecting object and face detection, recognizing, and identifying faces using imperfect data remained relatively unexplored. This study focuses on an occluded dataset comprising Indonesian faces, wherein 'occluded' denotes facial data that lacks complete visibility-encompassing instances where objects obscure faces or are partially cropped. This investigation involves a

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Zhang, Jianming, Chaoquan Lu, Jin Wang, Lei Wang, and Xiao-Guang Yue. "Concrete Cracks Detection Based on FCN with Dilated Convolution." Applied Sciences 9, no. 13 (2019): 2686. http://dx.doi.org/10.3390/app9132686.

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In civil engineering, the stability of concrete is of great significance to safety of people’s life and property, so it is necessary to detect concrete damage effectively. In this paper, we treat crack detection on concrete surface as a semantic segmentation task that distinguishes background from crack at the pixel level. Inspired by Fully Convolutional Networks (FCN), we propose a full convolution network based on dilated convolution for concrete crack detection, which consists of an encoder and a decoder. Specifically, we first used the residual network to extract the feature maps of the in

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Surkov, Yuriy I., Isabella A. Serebryakova, Yana K. Kuzinova, et al. "Multimodal Method for Differentiating Various Clinical Forms of Basal Cell Carcinoma and Benign Neoplasms In Vivo." Diagnostics 14, no. 2 (2024): 202. http://dx.doi.org/10.3390/diagnostics14020202.

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Correct classification of skin lesions is a key step in skin cancer screening, which requires high accuracy and interpretability. This paper proposes a multimodal method for differentiating various clinical forms of basal cell carcinoma and benign neoplasms that includes machine learning. This study was conducted on 37 neoplasms, including benign neoplasms and five different clinical forms of basal cell carcinoma. The proposed multimodal screening method combines diffuse reflectance spectroscopy, optical coherence tomography and high-frequency ultrasound. Using diffuse reflectance spectroscopy

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You-Jie Chen, Tai-Been Chen, and Wen‑Hung Twan. "Advanced Gallbladder Segmentation in Dynamic Ultrasound Imaging Using Fully Convolutional Networks." Emerging Science Innovation 4 (September 6, 2024): 33–40. https://doi.org/10.46604/emsi.2024.13650.

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This study develops an advanced technique for segmenting the gallbladder from dynamic B-mode ultrasound images to enhance the accuracy and efficiency of volumetric analysis in medical diagnostics. Using a Wi-Fi probe, volumetric data are captured and processed into labeled images for training a fully convolutional network (FCN) model with specifications including an epoch of 9, a batch size of 3, and a learning rate of 0.001. Performance metrics such as global accuracy, mean accuracy, and Intersection over Union (IoU) are evaluated. The MobileNetV2 architecture achieves a maximum mean IoU of 0

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Liu, Ri, Shubin Yang, Wansha Tang, Jie Yuan, Qiqing Chan, and Yunchuan Yang. "Multi-Task Environmental Perception Methods for Autonomous Driving." Sensors 24, no. 17 (2024): 5552. http://dx.doi.org/10.3390/s24175552.

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In autonomous driving, environmental perception technology often encounters challenges such as false positives, missed detections, and low accuracy, particularly in detecting small objects and complex scenarios. Existing algorithms frequently suffer from issues like feature redundancy, insufficient contextual interaction, and inadequate information fusion, making it difficult to perform multi-task detection and segmentation efficiently. To address these challenges, this paper proposes an end-to-end multi-task environmental perception model named YOLO-Mg, designed to simultaneously perform traf

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Zhao, Gang, and Dian Wang. "A Multiple Criteria Decision-Making Method Generated by the Space Colonization Algorithm for Automated Pruning Strategies of Trees." AgriEngineering 6, no. 1 (2024): 539–54. http://dx.doi.org/10.3390/agriengineering6010033.

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The rise of mechanical automation in orchards has sparked research interest in developing robots capable of autonomous tree pruning operations. To achieve accurate pruning outcomes, these robots require robust perception systems that can reconstruct three-dimensional tree characteristics and execute appropriate pruning strategies. Three-dimensional modeling plays a crucial role in enabling accurate pruning outcomes. This paper introduces a specialized tree modeling approach using the space colonization algorithm (SCA) tailored for pruning. The proposed method extends SCA to operate in three-di

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Lee, Donghyeon, Eunho Lee, and Youngbae Hwang. "Lossless Reconstruction of Convolutional Neural Network for Channel-Based Network Pruning." Sensors 23, no. 4 (2023): 2102. http://dx.doi.org/10.3390/s23042102.

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Network pruning reduces the number of parameters and computational costs of convolutional neural networks while maintaining high performance. Although existing pruning methods have achieved excellent results, they do not consider reconstruction after pruning in order to apply the network to actual devices. This study proposes a reconstruction process for channel-based network pruning. For lossless reconstruction, we focus on three components of the network: the residual block, skip connection, and convolution layer. Union operation and index alignment are applied to the residual block and skip

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Liu, Jie, Deyuan Li, and Xin Xu. "Enhancing bridge damage detection with Mamba-Enhanced HRNet for semantic segmentation." PLOS ONE 19, no. 10 (2024): e0312136. http://dx.doi.org/10.1371/journal.pone.0312136.

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With the acceleration of urbanization, bridges, as crucial infrastructure, their structural health and stability are paramount to public safety. This paper proposes Mamba-Enhanced HRNet for bridge damage detection. Mamba-Enhanced HRNet integrates the advantages of HRNet’s multi-resolution parallel design and VMamba’s visual state space model. By replacing the residual convolutional blocks in HRNet with a combination of VSS blocks and convolution, this model enhances the network’s capability to capture global contextual information while maintaining computational efficiency. This work builds an

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Shrey, Vasu Kapoor, and Vinayak Sharma. "Object Detection in Unstructured Driving Environments." International Journal for Research Publication and Seminar 15, no. 3 (2024): 136–41. http://dx.doi.org/10.36676/jrps.v15.i3.1459.

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This paper conducts a comprehensive error analysis of the inference process performed on the YOLOv8 and RTDETR model, utilizing two distinct datasets: MS COCO, on which YOLOv8 and RT-DETR is originally trained, and IDD, a separate dataset. The primary focus lies on evaluating model performance using mean Average Precision (mAP) and Intersection over Union (IoU) metrics. Through rigorous experimentation and analysis, we investigate the discrepancies in model performance when applied to these diverse datasets. The findings shed light on the strengths and weaknesses of the YOLOv8 and RT-DETR mode

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