PCB Defect Detection Algorithm Based on Improved YOLOv8

Abstract

In order to solve the performance and efficiency problems in PCB defect detection tasks, a PCB defect detection algorithm based on improved YOLOv8 is proposed, which aims to improve detection accuracy, reduce model complexity, adapt to small target detection, and operate in resource-constrained environments. achieve efficient detection. First, an improved neck network structure is introduced, which reduces the number of parameters and computational complexity of the model and improves resource utilization. Subsequently, the ShuffleAttention and BiFPN structure were added to enhance the model's multi-scale feature fusion capabilities and better adapt to small target detection. Finally, the WIoU loss function is used to replace the traditional CIoU loss function, thereby improving the detection accuracy and robustness of the model. Experimental results show that the improved algorithm achieved 94.2% and 49.0% in mAP50 and mAP90-95 respectively. The number of parameters, GFLOPs and weight size of the model were reduced by 33%, 12% and 32% respectively, reaching 1.992M, 7.1 and 4.2M. Provides an efficient, accurate and lightweight PCB defect detection solution, making it ideal for resource-constrained environments such as mobile devices and embedded systems.