YOLO-iDAT: An Improved YOLOv11 for PCB Defect Detection

Abstract

Printed Circuit Boards (PCB) are a fundamental component of electronic devices, serving to connect and support electronic components. They are widely used in various fields, including Consumer Electronics, Industrial Control, and Aerospace. However, modern PCBs are trending toward miniaturization, lightweight design, and high density. Consequently, tiny surface defects appear more frequently. Defects such as missing holes, mouse bites, and open circuits exhibit low contrast, making them challenging to detect. Traditional inspection methods suffer from several limitations. Manual visual inspection and electrical testing are inefficient. They suffer from low accuracy, poor efficiency, and high costs. The automated optical inspection (AOI) still needs people’s assistance. In addition, the early deep learning models like the initial versions of YOLO do not accurately detect small defects. During upsampling, these models lose fine-grained details and fail to adapt to unbalanced data. Consequently, they often fail to detect low-contrast defects. To address these issues, this paper proposes YOLO-iDAT. It is an improved version of YOLOv11. First, this paper changes the C2PSA module into C2PSA_iRMB. This new module combines the iRMB structure, SE attention and residual connections. It makes the model mix the global and local information better. And it significantly enhances the ability to 
extract the tiny defect feature. Second, this paper uses DySample for up sampling. It is a dynamic method. It prevents the blurring of small defects that often occurs with conventional upsampling methods. It keeps the feature clear. And it does not make the model slow. Third, this paper uses the Adaptive Threshold Focal Loss (ATFL) function. It uses an adaptive threshold and adjusts parameters to adapt to unbalanced data. It significantly reduces the number of missed defects. This paper uses the proposed model to test the public PKU-Market-PCB dataset. The results show that YOLO-iDAT gets a precision of 0.941, the mAP50 of 0.93 and the recall of 0.895. These represent improvements of 0.9%, 1%, and 1.8%, respectively, over the original YOLOv11. And the speed of our model is very fast. It gets FPS of 88.76. This is a sufficient speed for real-time detection in factory settings. These results demonstrate that YOLO-iDAT fits the requirement of industrial PCB defect detection. It provides a more accurate and more efficient solution for PCB defect detection.