测试技术学报2025，Vol.39Issue(1)：81-87,7.DOI:10.62756/csjs.1671-7449.2025012
基于改进YOLOv7-tiny的PCB表面缺陷检测
PCB Surface Defect Detection Based on Improved YOLOv7-tiny
解琳 1韩跃平 1翟倩 1李瑞红2
作者信息
- 1. 中北大学 信息与通信工程学院,山西 太原 030051
- 2. 中北大学 软件学院,山西 太原 030051
- 折叠
摘要
Abstract
Realizing real-time printed circuit board(PCB)surface defect detection is the basis for improving the intelligence of the PCB fabrication process.Aiming at the original PCB inspection method which is time-consuming,low-accuracy and not easy to move,this paper proposes an improved model based on YOLOv7-tiny.Replace the SiLU activation function in YOLOv7-tiny with the ELU function;introduce a centralized integrated convolutional module(C3 module),and combine depthwise separable convolution with C3 to form a centralized integrated depthwise separable convolution module;and add a convolutional block attention module.Experimentally,the improved model performs well in detection accuracy,recall rate,and mean average precision,and the size of the model drops by 2.8 MB compared to the original model.It also shows better detection results when compared with other mainstream target detection schemes.The ability of the improved YOLOv7-tiny to maintain higher accuracy while also reducing the memory requirements of the model opens up new possibilities for real-time detection of PCB defects as well as edge deployment.关键词
目标检测/YOLOv7-tiny/激活函数/集中综合深度可分离模块/注意力机制Key words
target detection/YOLOv7-tiny/activation function/centralized comprehensive depth sepa-rable module/attention mechanism分类
信息技术与安全科学引用本文复制引用
解琳,韩跃平,翟倩,李瑞红..基于改进YOLOv7-tiny的PCB表面缺陷检测[J].测试技术学报,2025,39(1):81-87,7.
