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Relevant bibliographies by topics / Depth-wise convolution

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Academic literature on the topic 'Depth-wise convolution'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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Journal articles on the topic "Depth-wise convolution"

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Hossain, Syed Mohammad Minhaz, Kaushik Deb, Pranab Kumar Dhar, and Takeshi Koshiba. "Plant Leaf Disease Recognition Using Depth-Wise Separable Convolution-Based Models." Symmetry 13, no. 3 (March 21, 2021): 511. http://dx.doi.org/10.3390/sym13030511.

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Proper plant leaf disease (PLD) detection is challenging in complex backgrounds and under different capture conditions. For this reason, initially, modified adaptive centroid-based segmentation (ACS) is used to trace the proper region of interest (ROI). Automatic initialization of the number of clusters (K) using modified ACS before recognition increases tracing ROI’s scalability even for symmetrical features in various plants. Besides, convolutional neural network (CNN)-based PLD recognition models achieve adequate accuracy to some extent. However, memory requirements (large-scaled parameters) and the high computational cost of CNN-based PLD models are burning issues for the memory restricted mobile and IoT-based devices. Therefore, after tracing ROIs, three proposed depth-wise separable convolutional PLD (DSCPLD) models, such as segmented modified DSCPLD (S-modified MobileNet), segmented reduced DSCPLD (S-reduced MobileNet), and segmented extended DSCPLD (S-extended MobileNet), are utilized to represent the constructive trade-off among accuracy, model size, and computational latency. Moreover, we have compared our proposed DSCPLD recognition models with state-of-the-art models, such as MobileNet, VGG16, VGG19, and AlexNet. Among segmented-based DSCPLD models, S-modified MobileNet achieves the best accuracy of 99.55% and F1-sore of 97.07%. Besides, we have simulated our DSCPLD models using both full plant leaf images and segmented plant leaf images and conclude that, after using modified ACS, all models increase their accuracy and F1-score. Furthermore, a new plant leaf dataset containing 6580 images of eight plants was used to experiment with several depth-wise separable convolution models.

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Kim, Daehee, Juhee Kang, and Jaekoo Lee. "Lightweighting of Super-Resolution Model Using Depth-Wise Separable Convolution." Journal of Korean Institute of Communications and Information Sciences 46, no. 4 (April 30, 2021): 591–97. http://dx.doi.org/10.7840/kics.2021.46.4.591.

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Zhang, Ke, Ken Cheng, Jingjing Li, and Yuanyuan Peng. "A Channel Pruning Algorithm Based on Depth-Wise Separable Convolution Unit." IEEE Access 7 (2019): 173294–309. http://dx.doi.org/10.1109/access.2019.2956976.

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Siddiqua, Shahzia, Naveena Chikkaguddaiah, Sunilkumar S. Manvi, and Manjunath Aradhya. "AksharaNet: A GPU Accelerated Modified Depth-Wise Separable Convolution for Kannada Text Classification." Revue d'Intelligence Artificielle 35, no. 2 (April 30, 2021): 145–52. http://dx.doi.org/10.18280/ria.350206.

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For content-based indexing and retrieval applications, text characters embedded in images are a rich source of information. Owing to their different shapes, grayscale values, and dynamic backgrounds, these text characters in scene images are difficult to detect and classify. The complexity increases when the text involved is a vernacular language like Kannada. Despite advances in deep learning neural networks (DLNN), there is a dearth of fast and effective models to classify scene text images and the availability of a large-scale Kannada scene character dataset to train them. In this paper, two key contributions are proposed, AksharaNet, a graphical processing unit (GPU) accelerated modified convolution neural network architecture consisting of linearly inverted depth-wise separable convolutions and a Kannada Scene Individual Character (KSIC) dataset which is grounds-up curated consisting of 46,800 images. From results it is observed AksharaNet outperforms four other well-established models by 1.5% on CPU and 1.9% on GPU. The result can be directly attributed to the quality of the developed KSIC dataset. Early stopping decisions at 25% and 50% epoch with good and bad accuracies for complex and light models are discussed. Also, useful findings concerning learning rate drop factor and its ideal application period for application are enumerated.

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Chao, Xiaofei, Xiao Hu, Jingze Feng, Zhao Zhang, Meili Wang, and Dongjian He. "Construction of Apple Leaf Diseases Identification Networks Based on Xception Fused by SE Module." Applied Sciences 11, no. 10 (May 18, 2021): 4614. http://dx.doi.org/10.3390/app11104614.

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The fast and accurate identification of apple leaf diseases is beneficial for disease control and management of apple orchards. An improved network for apple leaf disease classification and a lightweight model for mobile terminal usage was designed in this paper. First, we proposed SE-DEEP block to fuse the Squeeze-and-Excitation (SE) module with the Xception network to get the SE_Xception network, where the SE module is inserted between the depth-wise convolution and point-wise convolution of the depth-wise separable convolution layer. Therefore, the feature channels from the lower layers could be directly weighted, which made the model more sensitive to the principal features of the classification task. Second, we designed a lightweight network, named SE_miniXception, by reducing the depth and width of SE_Xception. Experimental results show that the average classification accuracy of SE_Xception is 99.40%, which is 1.99% higher than Xception. The average classification accuracy of SE_miniXception is 97.01%, which is 1.60% and 1.22% higher than MobileNetV1 and ShuffleNet, respectively, while its number of parameters is less than those of MobileNet and ShuffleNet. The minimized network decreases the memory usage and FLOPs, and accelerates the recognition speed from 15 to 7 milliseconds per image. Our proposed SE-DEEP block provides a choice for improving network accuracy and our network compression scheme provides ideas to lightweight existing networks.

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Kate, Vandana, and Pragya Shukla. "Breast Cancer Image Multi-Classification Using Random Patch Aggregation and Depth-Wise Convolution based Deep-Net Model." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 01 (January 19, 2021): 83. http://dx.doi.org/10.3991/ijoe.v17i01.18513.

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Adapting the profound, deep convolutional neural network models for large image classification can result in the layout of network architectures with a large number of learnable parameters and tuning of those varied parameters can considerably grow the complexity of the model. To address this problem a convolutional Deep-Net Model based on the extraction of random patches and enforcing depth-wise convolutions is proposed for training and classification of widely known benchmark Breast Cancer histopathology images. The classification result of these patches is aggregated using majority vote casting in deciding the final image classification type. It has been observed that the proposed Deep-Net model implementation results when compared with classification results of the VGG Net(16 layers) learned features, outclasses in terms of accuracy when applied to breast tumor Histopathology images. The objective of this work is to examine and comprehensively analyze the sub-class classification performance of the proposed model across all optical magnification frontiers.

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Dang, Lanxue, Peidong Pang, and Jay Lee. "Depth-Wise Separable Convolution Neural Network with Residual Connection for Hyperspectral Image Classification." Remote Sensing 12, no. 20 (October 17, 2020): 3408. http://dx.doi.org/10.3390/rs12203408.

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The neural network-based hyperspectral images (HSI) classification model has a deep structure, which leads to the increase of training parameters, long training time, and excessive computational cost. The deepened network models are likely to cause the problem of gradient disappearance, which limits further improvement for its classification accuracy. To this end, a residual unit with fewer training parameters were constructed by combining the residual connection with the depth-wise separable convolution. With the increased depth of the network, the number of output channels of each residual unit increases linearly with a small amplitude. The deepened network can continuously extract the spectral and spatial features while building a cone network structure by stacking the residual units. At the end of executing the model, a 1 × 1 convolution layer combined with a global average pooling layer can be used to replace the traditional fully connected layer to complete the classification with reduced parameters needed in the network. Experiments were conducted on three benchmark HSI datasets: Indian Pines, Pavia University, and Kennedy Space Center. The overall classification accuracy was 98.85%, 99.58%, and 99.96% respectively. Compared with other classification methods, the proposed network model guarantees a higher classification accuracy while spending less time on training and testing sample sites.

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商, 丽娟. "Super-Resolution Reconstruction Algorithm for Cross-Module Based on Depth-Wise Separable Convolution." Journal of Image and Signal Processing 07, no. 02 (2018): 96–104. http://dx.doi.org/10.12677/jisp.2018.72011.

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Huang, Gangjin, Yuanliang Zhang, and Jiayu Ou. "Transfer remaining useful life estimation of bearing using depth-wise separable convolution recurrent network." Measurement 176 (May 2021): 109090. http://dx.doi.org/10.1016/j.measurement.2021.109090.

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Cho, Sung In, Jae Hyeon Park, and Suk-Ju Kang. "A Generative Adversarial Network-Based Image Denoiser Controlling Heterogeneous Losses." Sensors 21, no. 4 (February 8, 2021): 1191. http://dx.doi.org/10.3390/s21041191.

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We propose a novel generative adversarial network (GAN)-based image denoising method that utilizes heterogeneous losses. In order to improve the restoration quality of the structural information of the generator, the heterogeneous losses, including the structural loss in addition to the conventional mean squared error (MSE)-based loss, are used to train the generator. To maximize the improvements brought on by the heterogeneous losses, the strength of the structural loss is adaptively adjusted by the discriminator for each input patch. In addition, a depth wise separable convolution-based module that utilizes the dilated convolution and symmetric skip connection is used for the proposed GAN so as to reduce the computational complexity while providing improved denoising quality compared to the convolutional neural network (CNN) denoiser. The experiments showed that the proposed method improved visual information fidelity and feature similarity index values by up to 0.027 and 0.008, respectively, compared to the existing CNN denoiser.

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Dissertations / Theses on the topic "Depth-wise convolution"

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Elavarthi, Pradyumna. "Semantic Segmentation of RGB images for feature extraction in Real Time." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1573575765136448.

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Schennings, Jacob. "Deep Convolutional Neural Networks for Real-Time Single Frame Monocular Depth Estimation." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-336923.

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Vision based active safety systems have become more frequently occurring in modern vehicles to estimate depth of the objects ahead and for autonomous driving (AD) and advanced driver-assistance systems (ADAS). In this thesis a lightweight deep convolutional neural network performing real-time depth estimation on single monocular images is implemented and evaluated. Many of the vision based automatic brake systems in modern vehicles only detect pre-trained object types such as pedestrians and vehicles. These systems fail to detect general objects such as road debris and roadside obstacles. In stereo vision systems the problem is resolved by calculating a disparity image from the stereo image pair to extract depth information. The distance to an object can also be determined using radar and LiDAR systems. By using this depth information the system performs necessary actions to avoid collisions with objects that are determined to be too close. However, these systems are also more expensive than a regular mono camera system and are therefore not very common in the average consumer car. By implementing robust depth estimation in mono vision systems the benefits from active safety systems could be utilized by a larger segment of the vehicle fleet. This could drastically reduce human error related traffic accidents and possibly save many lives. The network architecture evaluated in this thesis is more lightweight than other CNN architectures previously used for monocular depth estimation. The proposed architecture is therefore preferable to use on computationally lightweight systems. The network solves a supervised regression problem during the training procedure in order to produce a pixel-wise depth estimation map. The network was trained using a sparse ground truth image with spatially incoherent and discontinuous data and output a dense spatially coherent and continuous depth map prediction. The spatially incoherent ground truth posed a problem of discontinuity that was addressed by a masked loss function with regularization. The network was able to predict a dense depth estimation on the KITTI dataset with close to state-of-the-art performance.

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Book chapters on the topic "Depth-wise convolution"

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Ruan, Li, Yuanjie Jiang, Chang Yang, Yiyang Xing, Limin Xiao, and Xiangwen Qu. "Fast and Robust Image Matching Based on Depth-Wise Convolution Features and Unique Nearest Neighbour Similarity." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 933–40. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70665-4_100.

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Jeny, Afsana Ahsan, Masum Shah Junayed, Syeda Tanjila Atik, and Sazzad Mahamd. "A Model for Identifying Historical Landmarks of Bangladesh from Image Content Using a Depth-Wise Convolutional Neural Network." In Advances in Intelligent Systems and Computing, 444–54. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16657-1_41.

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Conference papers on the topic "Depth-wise convolution"

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Wan, Songtai, Chih-Yu Hsu, Jie Li, and Ming Zhao. "Depth-Wise Convolution with Attention Neural Network (DWA) for Pneumonia Detection." In 2020 International Conference on Intelligent Computing, Automation and Systems (ICICAS). IEEE, 2020. http://dx.doi.org/10.1109/icicas51530.2020.00035.

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Zhou, XuJin, and JingLei Tang. "YOLOv3-DSN Object Detection Algorithm Based on Depth Wise Separable Convolution." In ICRSA 2021: 2021 4th International Conference on Robot Systems and Applications. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3467691.3467698.

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Wu, Yunpu, and Weidong Jin. "A Fault Diagnosis Scheme for High-Speed Train Bogie based on Depth-wise Convolution." In 2018 IEEE International Conference on Progress in Informatics and Computing (PIC). IEEE, 2018. http://dx.doi.org/10.1109/pic.2018.8706307.

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Fuhl, Wolfgang, and Enkelejda Kasneci. "Rotated Ring, Radial and Depth Wise Separable Radial Convolutions." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9534009.

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Beliaev, Stanislav, and Boris Ginsburg. "TalkNet: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis." In Interspeech 2021. ISCA: ISCA, 2021. http://dx.doi.org/10.21437/interspeech.2021-1770.

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Stephen, Okeke, Young Jick Jang, Tae Soo Yun, and Mangal Sain. "Depth-Wise Based Convolutional Neural Network for Street Imagery Digit Number Classification." In 2019 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). IEEE, 2019. http://dx.doi.org/10.1109/cse/euc.2019.00034.

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Han, Zhuo, Dongfei Wang, Aidong Men, and Yun Zhou. "An improved YOLOv2 model with depth-wise separable convolutional layers for object detection." In Tenth International Conference on Graphics and Image Processing (ICGIP 2018), edited by Hui Yu, Yifei Pu, Chunming Li, and Zhigeng Pan. SPIE, 2019. http://dx.doi.org/10.1117/12.2524181.

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Hussain, Abid, and Wang Hesheng. "Depth-Wise Pooling: A Parameter-Less Solution for Channel Reduction of Feature-Map in Convolutional Neural Network." In 2019 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE, 2019. http://dx.doi.org/10.1109/rcar47638.2019.9044014.

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Sarkhel, Ritesh, and Arnab Nandi. "Deterministic Routing between Layout Abstractions for Multi-Scale Classification of Visually Rich Documents." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/466.

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Classifying heterogeneous visually rich documents is a challenging task. Difficulty of this task increases even more if the maximum allowed inference turnaround time is constrained by a threshold. The increased overhead in inference cost, compared to the limited gain in classification capabilities make current multi-scale approaches infeasible in such scenarios. There are two major contributions of this work. First, we propose a spatial pyramid model to extract highly discriminative multi-scale feature descriptors from a visually rich document by leveraging the inherent hierarchy of its layout. Second, we propose a deterministic routing scheme for accelerating end-to-end inference by utilizing the spatial pyramid model. A depth-wise separable multi-column convolutional network is developed to enable our method. We evaluated the proposed approach on four publicly available, benchmark datasets of visually rich documents. Results suggest that our proposed approach demonstrates robust performance compared to the state-of-the-art methods in both classification accuracy and total inference turnaround.

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Cheng, Jingru, Deming Mao, Majid Salamah, and Roland Horne. "Scale Buildup Detection and Characterization in Production Wells by Deep Learning Methods." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205988-ms.

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Abstract This study developed an analytical tool for the detection and characterization of scale buildup from well data using deep learning methods. The developed method allows for a sensitive detection of the initiation of scaling as well as an accurate prediction of the magnitude of existing scale buildup. Scale deposition causes tubing ID decreases and therefore results in production declines, so a sensitive approach to detect the scale deposition is valuable to reduce the damage and losses due to this problem. The underlying deep learning methods are both single- and multioutput, deep neural networks that consist of a combination of convolutional, long short-term memory (LSTM) and fully connected layers. We trained the networks on more than 30 sets of well data, with the objective of predicting the presence and the magnitude of scale. We started with cases of full scale deposition over the whole wellbore depth, and then extended our study to partial depth scale deposition. We built up a point-wise neural network model combining two blocks, which each contain several fully-connected layers followed by an LSTM layer specifically focusing on relatively smaller or larger tubing ID changes, corresponding to more or less scale deposition. To characterize the segmented scale deposition, we transformed to a three-dimensional problem, which can be solved by extracting the tubing ID changes and the scale deposition segment length. The multioutput model was able to predict the tubing ID and volume changes at the same time, using a combination of convolutional and LSTM layers with residual network blocks and updated using a loss function that we defined. Tubing ID changes were extracted accurately with metric R-square more than 90%, while the length of the scale deposit could be classified into two classes (high scaling or low scaling) with good accuracy. Though existing physical and chemical methods can be used to analyze scale deposition, the methods are often applied after considerable production decrease has already occurred. By using deep learning algorithms, our study came up with a new way to predict the scaling problem in advance with high sensitivity.

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