Xiao Gaoshang, Hou Shuling, Zhou Huiying
School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, China.
Sci Rep. 2024 Mar 28;14(1):7351. doi: 10.1038/s41598-024-57491-3.
During the manufacturing process of printed circuit boards (PCBs), quality defects can occur, which can affect the performance and reliability of PCBs. Existing deep learning-based PCB defect detection methods are difficult to simultaneously achieve the goals of high detection accuracy, fast detection speed, and small number of parameters. Therefore, this paper proposes a PCB defect detection algorithm based on CDI-YOLO. Firstly, the coordinate attention mechanism (CA) is introduced to improve the backbone and neck network of YOLOv7-tiny, enhance the feature extraction capability of the model, and thus improve the accuracy of model detection. Secondly, DSConv is used to replace part of the common convolution in YOLOv7-tiny to achieve lower computing costs and faster detection speed. Finally, Inner-CIoU is used as the bounding box regression loss function of CDI-YOLO to speed up the bounding box regression process. The experimental results show that the method achieves 98.3% mAP on the PCB defect dataset, the detection speed is 128 frames per second (FPS), the parameters is 5.8 M, and the giga floating-point operations per second (GFLOPs) is 12.6 G. Compared with the existing methods, the comprehensive performance of this method has advantages.
在印刷电路板（PCB）的制造过程中，可能会出现质量缺陷，这会影响PCB的性能和可靠性。现有的基于深度学习的PCB缺陷检测方法难以同时实现高检测精度、快速检测速度和少量参数的目标。因此，本文提出了一种基于CDI-YOLO的PCB缺陷检测算法。首先，引入坐标注意力机制（CA）来改进YOLOv7-tiny的主干和颈部网络，增强模型的特征提取能力，从而提高模型检测的准确性。其次，使用DSConv替换YOLOv7-tiny中的部分普通卷积，以实现更低的计算成本和更快的检测速度。最后，使用Inner-CIoU作为CDI-YOLO的边界框回归损失函数，以加速边界框回归过程。实验结果表明，该方法在PCB缺陷数据集上实现了98.3%的平均精度均值（mAP），检测速度为每秒128帧（FPS），参数为5.8M，每秒千兆浮点运算次数（GFLOPs）为12.6G。与现有方法相比，该方法的综合性能具有优势。
