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Traffic Sign Recognition for Autonomous Vehicle Using Optimized YOLOv7 and convolutional block attention module

Computers, Materials & Continua 2023年第10期77卷 445-466页

作者： P.Kuppusamy M.Sanjay P.V.Deepashree C.Iwendi School of Computer Science and Engineering VIT-AP UniversityAndhra Pradesh522237India School of Creative Technology University of BoltonManchesterBL35ABUK

The infrastructure and construction of roads are crucial for the economic and social development of a region,but traffic-related challenges like accidents and congestion persist.Artificial Intelligence(AI)and Machine Learning(ML)have been used in road infrastructure and construction,particularly with the Internet of Things(IoT)devices.Object detection in Computer Vision also plays a key role in improving road infrastructure and addressing trafficrelated problems.This study aims to use You Only Look Once version 7(YOLOv7),convolutional block attention module(CBAM),the most optimized object-detection algorithm,to detect and identify traffic signs,and analyze effective combinations of adaptive optimizers like Adaptive Moment estimation(Adam),Root Mean Squared Propagation(RMSprop)and Stochastic Gradient Descent(SGD)with the YOLOv7.Using a portion of German traffic signs for training,the study investigates the feasibility of adopting smaller datasets while maintaining high accuracy.The model proposed in this study not only improves traffic safety by detecting traffic signs but also has the potential to contribute to the rapid development of autonomous vehicle systems.The study results showed an impressive accuracy of 99.7%when using a batch size of 8 and the Adam optimizer.This high level of accuracy demonstrates the effectiveness of the proposed model for the image classification task of traffic sign recognition.

关键词： Object detection traffic sign detection YOLOv7 convolutional block attention module road sign detection Adam

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ANC: attention Network for COVID-19 Explainable Diagnosis Based on convolutional block attention module

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Computer Modeling in Engineering & Sciences 2021年第6期127卷 1037-1058页

作者： Yudong Zhang Xin Zhang Weiguo Zhu Jiangsu Key Laboratory of Advanced Manufacturing Technology Huaiyin Institute of TechnologyHuai’an223003China Department of Medical Imaging The Fourth People’s Hospital of Huai’anHuai’an223002China School of Informatics University of LeicesterLeicesterLE17RHUK

Aim: To diagnose COVID-19 more efficiently and more correctly, this study proposed a novel attention network forCOVID-19 (ANC). Methods: Two datasets were used in this study. An 18-way data augmentation was proposed toavoid overfitting. Then, convolutional block attention module (CBAM) was integrated to our model, the structureof which is fine-tuned. Finally, Grad-CAM was used to provide an explainable diagnosis. Results: The accuracyof our ANC methods on two datasets are 96.32% ± 1.06%, and 96.00% ± 1.03%, respectively. Conclusions: Thisproposed ANC method is superior to 9 state-of-the-art approaches.

关键词： Deep learning convolutional block attention module attention mechanism COVID-19 explainable diagnosis

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Foreground Segmentation Network with Enhanced attention

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Journal of Shanghai Jiaotong university(Science) 2023年第3期28卷 360-369页

作者：姜锐朱瑞祥蔡萧萃苏虎 College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Research Center of Precision Sensing and Control Institute of AutomationChinese Academy of SciencesBeijing 100190China

Moving object segmentation (MOS) is one of the essential functions of the vision system of all robots,including medical robots. Deep learning-based MOS methods, especially deep end-to-end MOS methods, are actively investigated in this ﬁeld. Foreground segmentation networks (FgSegNets) are representative deep end-to-endMOS methods proposed recently. This study explores a new mechanism to improve the spatial feature learningcapability of FgSegNets with relatively few brought parameters. Speciﬁcally, we propose an enhanced attention(EA) module, a parallel connection of an attention module and a lightweight enhancement module, with sequentialattention and residual attention as special cases. We also propose integrating EA with FgSegNet_v2 by taking thelightweight convolutional block attention module as the attention module and plugging EA module after the twoMaxpooling layers of the encoder. The derived new model is named FgSegNet_v2 EA. The ablation study veriﬁesthe effectiveness of the proposed EA module and integration strategy. The results on the CDnet2014 dataset,which depicts human activities and vehicles captured in different scenes, show that FgSegNet_v2 EA outperformsFgSegNet_v2 by 0.08% and 14.5% under the settings of scene dependent evaluation and scene independent evaluation, respectively, which indicates the positive effect of EA on improving spatial feature learning capability ofFgSegNet_v2.

关键词： human-computer interaction moving object segmentation foreground segmentation network enhanced attention convolutional block attention module

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