基于改进YOLOv5的药用胶囊表面缺陷检测方法

目的 在质检过程中精确快速地检测到药用空心胶囊的表面缺陷。方法 基于YOLOv5算法，针对模型网络参数量大和对长距离依赖关系的学习能力较弱的问题，提出在主干网络部分引入GhostNet模块和坐标注意力机制，使网络有效捕捉数据位置信息和通道信息的关系。结果 实验结果表明，改进的网络结构能够在参数量下降为原来的57%的前提下，对药用胶囊表面的破损、印刷错误、孔洞、划痕、凹陷等5类缺陷的平均检测精度达到96.9%，相较于YOLOv5s提高了2.4个百分点，检测速度提升了12帧/s。结论 文中方法能够有效对药用胶囊表面缺陷进行分类和定位，提高缺陷检测的准确率。

Surface Defect Detection Method for Pharmaceutical Capsules Based on Modified YOLOv5

The work aims to detect the surface defects of pharmaceutical hollow capsules in quality inspection accurately and quickly. Based on YOLOv5 algorithm and aiming at the problems of large amount of model network parameters and weak learning ability of long-distance dependence, GhostNet module and Coordinate attention mechanism were introduced into the backbone network to make the network effectively capture the relationship between data location information and channel information. The experimental results showed that the improved network structure could accurately detect five kinds of defects such as damage, printing error, hole, scratch and depression on the surface of pharmaceutical capsule on the premise of decreasing to 57% of the original parameters. The average accuracy of each defect was 96.9%, which was increased by 2.4 percentage points. The detection speed was increased by 12 FPS. The proposed method can effectively classify and locate the surface defects of pharmaceutical capsules, and improve the accuracy of defect detection.