Research on commodity image detection based on improved YOLOv5

JiaPeng Lv; DeGuo Yang

doi:10.1117/12.2660139

3 February 2023 Research on commodity image detection based on improved YOLOv5

JiaPeng Lv, DeGuo Yang

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125111E (2023) https://doi.org/10.1117/12.2660139
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

In order to solve the problem of false detection and missing detection caused by different shapes and scales of commodities in commodity image detection, a commodity image detection algorithm based on improved YOLOv5 was proposed. Firstly, the algorithm optimizes the extraction features of Backbone by adding Transformer structure to Backbone network of YOLOv5. Then, the original PANet structure of Neck network is replaced by BiFPN structure, and the coordinate attention mechanism is introduced, so that the new network model (CA-BIFPN) can detect and locate the position of goods in the image more accurately. In order to verify the effectiveness of the improved method, a comparative experiment is conducted with YOLOv5. Experimental data show that the improved YOLOv5 algorithm proposed in this paper achieves multi-scale target detection, and the mAP reaches 99.5% on the self-made dataset, which is 0.2% higher than the original YOLOVv5 algorithm.

Citation Download Citation

JiaPeng Lv and DeGuo Yang "Research on commodity image detection based on improved YOLOv5", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125111E (3 February 2023); https://doi.org/10.1117/12.2660139

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