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SepFE:separable Fusion Enhanced Network for Retinal Vessel Segmentation

Computer Modeling in Engineering & Sciences 2023年第9期136卷 2465-2485页

作者： Yun Wu Ge Jiao Jiahao Liu College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

The accurate and automatic segmentation of retinal vessels fromfundus images is critical for the early diagnosis and prevention ofmany eye diseases,such as diabetic retinopathy(DR).Existing retinal vessel segmentation approaches based on convolutional neural networks(CNNs)have achieved remarkable ***,we extend a retinal vessel segmentation model with low complexity and high performance based on U-Net,which is one of the most popular *** view of the excellent work of depth-wise separable convolution,we introduce it to replace the standard convolutional *** complexity of the proposed model is reduced by decreasing the number of parameters and calculations required for *** ensure performance while lowering redundant parameters,we integrate the pre-trained MobileNet V2 into the ***,a feature fusion residual module(FFRM)is designed to facilitate complementary strengths by enhancing the effective fusion between adjacent levels,which alleviates extraneous clutter introduced by direct ***,we provide detailed comparisons between the proposed SepFE and U-Net in three retinal image mainstream datasets(DRIVE,STARE,and CHASEDB1).The results show that the number of SepFE parameters is only 3%of U-Net,the Flops are only 8%of U-Net,and better segmentation performance is *** superiority of SepFE is further demonstrated through comparisons with other advanced methods.

关键词： Retinal vessel segmentation U-Net depth-wise separable convolution feature fusion

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Sea-Land Segmentation of Remote Sensing Images Based on SDW-UNet

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Computer Systems Science & Engineering 2023年第5期45卷 1033-1045页

作者： Tianyu Liu Pengyu Liu Xiaowei Jia Shanji Chen Ying Ma Qian Gao The Information Department Beijing University of TechnologyBeijing 100124China School of Physics and Electronic Information Engineering Qinghai Minzu UniversityXining810000China Advanced Information Network Beijing Laboratory Beijing100124China Computational Intelligence and Intelligent Systems Beijing key Laboratory Beijing100124China Department of Computer Science University of Pittsburgh15260USA

Image segmentation of sea-land remote sensing images is of great importance for downstream applications including shoreline extraction,the monitoring of near-shore marine environment,and near-shore target *** mitigate large number of parameters and improve the segmentation accuracy,we propose a new Squeeze-depth-wise UNet(SDW-UNet)deep learning model for sea-land remote sensing image *** proposed SDW-UNet model leverages the squeeze-excitation and depth-wise separable convolution to construct new convolution modules,which enhance the model capacity in combining multiple channels and reduces the model *** further explore the effect of position-encoded information in NLP(Natural Language Processing)domain on sea-land segmentation *** have conducted extensive experiments to compare the proposed network with the mainstream segmentation network in terms of accuracy,the number of parameters and the time cost for *** test results on remote sensing data sets of Guam,Okinawa,Taiwan China,San Diego,and Diego Garcia demonstrate the effectiveness of SDW-UNet in recognizing different types of sea-land areas with a smaller number of parameters,reduces prediction time cost and improves performance over other mainstream segmentation *** also show that the position encoding can further improve the accuracy of model segmentation.

关键词： Sea-land segmentation UNet depth-wise separable convolution squeeze-excitation position encoding

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A Light-weight Deep Neural Network for Vehicle Detection in Complex Tunnel Environments

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Instrumentation 2023年第1期10卷 32-44页

作者： ZHENG Lie REN Dandan Hubei University of Technology Wuhan 430000

With the rapid development of social economy,transportation has become faster and more *** an important part of goods transportation,the safe maintenance of tunnel highways has become particularly *** maintenance of tunnel roads has become more difficult due to problems such as sealing,narrowness and lack of ***,target detection methods are advantageous in detecting tunnel vehicles in a timely manner through ***,in order to prevent vehicle misdetection and missed detection in this complex environment,we propose aYOLOv5-Vehicle model based on the YOLOv5 *** model is improved in three ***,The backbone network of YOLOv5 is replaced by the lightweight MobileNetV3 network to extract features,which reduces the number of model parameters;Next,all convolutions in the neck module are improved to the depth-wise separable convolutions to further reduce the number of model parameters and computation,and improve the detection speed of the model;Finally,to ensure the accuracy of the model,the CBAM attention mechanism is introduced to improve the detection accuracy and precision of the *** results demonstrate that the YOLOv5-Vehicle model can improve the accuracy.

关键词： CBAM depth-wise separable convolution MobileNetV3 Vehicle Detection YOLOV5

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A Lightweight Improved U-Net with Shallow Features Combination and Its Application to Defect Detection

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Wuhan University Journal of Natural Sciences 2020年第5期25卷 461-468页

作者： WU Hong SUN Xiankur XIONG Yujie School of Electronic and Electrical Engineering Shanghai University of Engineering ScienceShanghai 201620China

In order to solve the problems of shallow features loss and high computation cost of U-Net,we propose a lightweight with shallow features combination(IU-Net).IU-Net adds several convolution layers and short links to the skip path to extract more shallow *** the same time,the original convolution is replaced by the depth-wise separable convolution to reduce the calculation cost and the number of ***-Net is applied to detecting small metal industrial products *** is evaluated on our own SUES-Washer dataset to verify the *** results demonstrate that our proposed method outperforms the original U-Net,and it has 1.73%,2.08%and 11.2%improvement in the intersection over union,accuracy,and detection time,respectively,which satisfies the requirements of industrial detection.

关键词： U-Net depth-wise separable convolution shallow features combination defect detection

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Validation Research on the Application of depthwise separable convolutional Al Facial Expression Recognition in Non-pharmacological Treatment of BPSD

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Journal of Clinical and Nursing Research 2021年第4期5卷 31-37页

作者： Xiangyu Liu Doctoral Course Graduate School of Human SciencesWaseda UniversityJapan

One of the most obvious clinical reasons of dementia or The Behavioral and Psychological Symptoms of Dementia(BPSD)are the lack of emotional expression,the increased frequency of negative emotions,and the impermanence of *** the reduction of BPSD in dementia through emotions can be considered effective and widely used in the field of non-pharmacological *** present,this article will verify whether the image recognition artificial intelligence(AI)system can correctly reflect the emotional performance of the elderly with dementia through a questionnaire survey of three professional elderly nursing *** ANOVA(sig.=0.50)is used to determine that the judgment given by the nursing staff has no obvious deviation,and then Kendall's test(0.722**)and spearman's test(0.863**)are used to verify the judgment severity of the emotion recognition system and the nursing staff *** implies the usability of the ***,it can be expected to be further applied in the research related to BPSD elderly emotion detection.

关键词： depth-wise separable convolution Emotion BPSD Dementia Nursing

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