针对目前仓储货物分类速度慢、易出错、灵活性差等问题，提出了一种改进YOLOv5s的货物检测算法，对仓储货物进行预分类。首先，根据仓储货物的外形特征，将其分为包装箱与包装袋两大类，形成训练数据集;其次，将骨干网络更换为具有更小模型尺寸的MobileNetV3，加快推理;再次，添加SE注意力机制模块，旨在提高模型的检测精度;最后，结合α_CIoU损失函数，增强模型的灵活度。通过实验验证，改进后的算法相较于原始算法在精确率(Precision，P)、平均类别精度(mean Average precision，mAP)和帧率(Frames per second，FPS)三方面分别提升2。1％、0。5％和10。6％，能够高效地完成对仓储货物的预分类工作。
Research on an Improved YOLOv5s-based Algorithm for Warehouse Goods Detection
A modified YOLOv5s detection algorithm had 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 were divided into two main categories:packaging boxes and packaging bags,forming a training dataset.Secondly,the backbone network was replaced with MobileNetV3,a smaller-sized model,to accelerate inference.Additionally,an SE attention mechanism module was added to enhance the detection accuracy of the model.Finally,the α_CIoU loss function was incorporated to improve the flexibility of the model.Experimental results demonstrated 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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