基于改进 YOLOv5s 的烟丝制丝生产线小目标杂物检测方法

文章提出一种基于改进 YOLOv5s 的小目标检测算法，以 YOLOv5s 算法为基础模型，首先将 Focal Loss 和 EIoU Loss 引入模型优化原有的 BCE Loss 和 CIoU Loss，加快模型的收敛速度；其次添加一个目标检测头，提高对小目标杂物的检测精度；最后对比分析不同类型注意力模块对模型的影响，并将坐标注意力引入模型颈部，加强模型对目标关键特征的提取，提高模型的学习能力。基于自制的杂物数据集对模型进行训练，实验结果表明，相较 YOLOv5s 算法，改进后的模型在测试集上的精确率、召回率、平均精度均值（mean average precision，mAP）值分别提高 4.9%、5.5%、7.3%，识别效果更好，满足实际生产中精确性和实时性要求。

A small target detection algorithm based on the improved YOLOv5s is proposed. Firstly, based on YOLOv5s, Focal Loss and EIoU Loss are introduced into the model to optimize the original BCE Loss and CIoU Loss to accelerate the convergence speed of the model. Secondly, a target detection head is added to improve the detection accuracy of small target sundries. Finally, the influence of different types of attention modules on the model is compared and analyzed, and coordinate attention is introduced into the model neck to strengthen the extraction of key features of the target and improve the learning ability of the model. The model is trained based on the self-made sundries dataset, and the experimental results show that compared with YOLOv5s, the accuracy of the improved model on the test set is improved by 4.9%, the recall rate increases by 5.5%, and the mean average precision (mAP) value increases by 7.3%. The model has better recognition effect, which meets the accuracy and real-time requirements in actual production.