目的 针对小尺寸和特征不明显的纸杯缺陷在检测过程中易出现漏检、错检的问题，提出一种基于改进YOLOv5s模型纸杯缺陷检测方法。方法 在原始模型的Backbone部分引入CBAM注意力机制模块，提升模型的特征提取能力;增加一个YOLO检测头，将三尺度检测改为四尺度检测，提高模型对小目标和特征不明显目标的检测能力;在Neck部分借鉴加权双向特征金字塔网络BiFPN，对原始模型中的PANet进行部分改进，加强模型的特征融合能力。结果 结果显示，改进后的模型YOLOv5s–CXO精度为89.1%、召回率为90.4%、平均精度均值为89.5%，比原始模型的精度提高了1.5%、召回率提高了1.3%、平均精度均值提高了1.2%。结论 本文的改进方法有效提高了模型的检测能力，对小尺寸和特征不明显纸杯缺陷的检测效果有明显提升。

The work aims to propose a paper cup defect detection method based on improved YOLOv5s model aiming at the problem that paper cup defects in small size and with insignificant features are easy to be missed and misdetected in the detection process. The CBAM attention mechanism module was introduced in the Backbone part of the original model to improve the feature extraction ability of the model. A YOLO detection head was added, and the three-scale detection was changed to four-scale detection to improve the detection ability of the model for small targets and targets with insignificant features. In the Neck part, the weighted bidirectional feature pyramid network BiFPN was used to partially improve the PANet in the original model to strengthen the feature fusion ability of the model. The results indicated that the precision of the improved model YOLOv5s-CXO was 89.1%, the recall was 90.4%, and the average precision mean was 89.5%. Compared with the original model, the precision was increased by 1.5%, the recall was increased by 1.3%, and the average precision mean was increased by 1.2%. The proposed improved method effectively improves the detection ability of the model, and significantly improves the detection effect of paper cup defects in small size and with insignificant features.