Abstract
As an excellent algorithm, YOLOv5 is an object detection model with the advantages of high flexibility and fast speed, but it has problems with many network parameters, complex model structure, and low boundary regression accuracy for the target. For the above problems, this study improves on the YOLOv5s algorithm and proposes a new model YOLO-GC with lower hardware requirements, fewer network parameters and higher boundary regression accuracy. First, the upsampling module of YOLOv5s is replaced by the CARAFE upsampling module to increase the receptive field and semantic features, the Ghost module to reduce the number of parameters and calculation, and the CBAM attention mechanism to combine the spatial and channel attention map, the model pays more attention to the key areas to improve the model accuracy. The test results of this research method on the PASCAL VOC object detection benchmark dataset show that compared with YOLOv5s, the number of parameters is reduced by 44.2%, the model size is reduced by 42.8%, the is increased by 1.2%, and the :0.95 An increase of 5.5%.
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Acknowledgment
This work is supported by Fujian Provincial Natural Science Foundation (Grant No.: 2021J1851) and Xiamen Winjoin Technology Co. (Contract No.: S21228).
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Chen, N., Li, Q., Ning, J., Wang, Q., Liao, N. (2023). Improving the Lightweight Object Detection Method for YOLOv5. In: Wang, Y., Yu, T., Wang, K. (eds) Advanced Manufacturing and Automation XII. IWAMA 2022. Lecture Notes in Electrical Engineering, vol 994. Springer, Singapore. https://doi.org/10.1007/978-981-19-9338-1_4
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DOI: https://doi.org/10.1007/978-981-19-9338-1_4
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