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| Research on an Improved YOLOv5s-based Algorithm for Warehouse Goods Detection |
| WANG Ying1 , WANG Chen1 , JIA Yongtao2 , LIU Qi1
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| School of Information and Control Engineering,Jilin Institute of Chemical Technology, Jilin City 132022,China |
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Abstract 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.
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Published: 25 January 2024
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