%A WANG Ying , WANG Chen , JIA Yongtao , LIU Qi
%T Research on an Improved YOLOv5s-based Algorithm for Warehouse Goods Detection
%0 Journal Article
%D 2024
%J Journal of Jilin Institute of Chemical Technology
%R 10.16039/j.cnki.cn22-1249.2024.01.009
%P 51-58
%V 41
%N 1
%U {https://xuebao.jlict.edu.cn/CN/abstract/article_2237.shtml}
%8 2024-01-25
%X A modified YOLOv5s detection algorithm has been proposed to address the issues of slow classification speed, error-proneness, and low flexibility in warehouse goods categorization. The algorithm aims to pre-classify warehouse goods. Firstly, based on the external characteristics of warehouse goods, they are divided into two main categories: packaging boxes and packaging bags, forming a training dataset. Secondly, the backbone network is replaced with MobileNetV3, a smaller-sized model, to accelerate inference. Additionally, an SE attention mechanism module is added to enhance the detection accuracy of the model. Finally, the α_CIoU loss function is incorporated to improve the flexibility of the model. Experimental results demonstrate that the improved algorithm achieves a 2.1% increase in precision (P), a 0.5% increase in mean Average Precision (mAP), and a 10.6% increase in Frames Per Second (FPS) compared to the original algorithm. It enables efficient pre-classification of warehouse goods.