Abstract
Object detection is an important fundamental problem in computer vision research, and is also the basis for other high-level visual tasks such as object tracking, behavioral analysis, and image description. Since target recognition is the simultaneous recognition and assessment of multiple targets, the accuracy of target recognition is generally low. In order to improve the accuracy of target detection, this paper introduces the Efficient Channel Attention (ECA) module to optimize the original yolov5 model, which has fewer parameters and significantly improved performance. After the ECA module performs channel-by-channel global average pooling without reducing the dimensionality, it considers each channel and its k neighbors to capture local cross-channel interactions, which enhances the discrimination of features. Experiments on the coco data set show that the improved yolov5 model based on the ECA module proposed in this paper can be used 68.84% accuracy to detect these object. Compared with the comparison algorithm, the training accuracy of the yolov5 model integrated with the ECA module is improved 3.02%, which proves that the ECA module can further improve the accuracy of yolov5, and can meet the requirements of reliability and stability of target detection.
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Acknowledgments
This work was supported in part by the Tianshan Young Talent Program, Xinjiang Uygur Autonomous Region under Grant 2018Q024, in part by the Natural Science Foundation of China under Grant 61771089 and Grant 61961040, and in part by the Regional Cooperative Innovation Program of Autonomous Region (Aid Program of Science and Technology to Xinjiang) under Grant 2020E0247 and Grant 2019E0214.
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Chen, S., Chen, B. (2022). Research on Object Detection Algorithm Based on Improved Yolov5. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z. (eds) Artificial Intelligence in China. Lecture Notes in Electrical Engineering, vol 854. Springer, Singapore. https://doi.org/10.1007/978-981-16-9423-3_37
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DOI: https://doi.org/10.1007/978-981-16-9423-3_37
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