基于GhostNet-YOLOv4算法的印刷电路板缺陷检测

针对印刷电路板表面面积小而且上面电子器件焊点众多,传统检测方法很难进行有效检测的问题,提出了一种基于GhostNet-YOLOv4的印刷电路板表面焊点检测算法。首先,修改了YOLOv4算法的主干网络以增强特征提取能力,其次加入注意力机制使网络更注重缺陷特征,用GhostNet代替CSPDarknet53作为主干网络。此算法相比于传统的印刷电路板检测算法提高了检测精度和检测速度,可以实现对印刷电路板表面常见的断路、漏焊、短路等缺陷的精确检测和迅速分类。通过对印刷电路板数据集的检测结果分析表明,该改进算法具有较好的实用性,在测试集上的平均精度为86.68%,FPS达到了25.43,可以满足印刷电路板实际检测需求。

Defect detection of printed circuit board based on GhostNet-YOLOv4 algorithm

Aiming at the problem that the area of printed circuit board is small and there are many electronic device solder joints on it, which is difficult to detect effectively by traditional detection methods, a surface solder joint detection algorithm of printed circuit board based on GhostNet-YOLOv4 is proposed. First, the backbone network of YOLOv4 is modified to enhance the feature extraction capability. Secondly, adding attention mechanism makes the network pay more attention to defect features. Finally, use GhostNet instead of CSPDarknet53 as the backbone network. Compared with the traditional PCB detection algorithm, this algorithm improves the detection accuracy and speed, and can realize the accurate detection and rapid classification of common defects such as broken circuit, missing welding and short circuit on the surface of PCB. Experiments on PCB data sets show that: the improved algorithm has good practicability, the accuracy on the test set is 86.68%, FPS reached 25.43, can meet the actual detection requirements of printed circuit boards.