YOLOv5 improved algorithm and its application in real-time inventory of corrugated paper

Qian Wu; Qinhao Kang; Yuxiang Yang; Lin Li; Yu Han

doi:10.1117/12.3035375

19 July 2024 YOLOv5 improved algorithm and its application in real-time inventory of corrugated paper

Qian Wu, Qinhao Kang, Yuxiang Yang, Lin Li, Yu Han

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132133C (2024) https://doi.org/10.1117/12.3035375
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

To detect corrugated paper quickly and accurately, a YOLOv5-based algorithm has been created. In order to improve the fusion of lower layer feature information, the operation of GSConv layer is introduced in the feature enhancement network algorithm. for better utilizing feature semantic information, the CARAFE upsampling operator is used instead of the original upsampling. MobileNetv3 was used to replace its backbone network to reduce computing costs and achieve lightweighting. Using the corrugated paper dataset constructed by the authors as an example, the improved algorithm was found to have a parameter size of 1.3 M based on the results., computational complexity of 1.8 GFLOPS, and weight size of 2.9 MB. Compared to the original YOLOv5n algorithm, these values decreased by 27.8%, 56.1%, and 21.6%. Its key technical indicators mAP@0.5 and FPS can be as high as 88.0% and 68.5 respectively. These results show that the improved algorithm satisfies the requirements for on-site deployment on mobile devices. The research shows that applying improved algorithms to conduct real-time and accurate inventory of corrugated packaging is a feasible approach.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qian Wu, Qinhao Kang, Yuxiang Yang, Lin Li, and Yu Han "YOLOv5 improved algorithm and its application in real-time inventory of corrugated paper", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132133C (19 July 2024); https://doi.org/10.1117/12.3035375

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