Abstract
With the rapid development of industrial automation and intelligent manufacturing, defect detection of electronic products has become crucial in the production process. Traditional defect detection methods often face the problems of insufficient accuracy and inefficiency when dealing with complex backgrounds, tiny defects, and multiple defect types. To overcome these problems, this paper proposes Y-MaskNet, a multi-task joint learning framework based on YOLOv5 and Mask R-CNN, which aims to improve the accuracy and efficiency of defect detection and segmentation in electronic products. Y-MaskNet combines the high efficiency of YOLOv5 in target detection with the fine segmentation capability of Mask R-CNN and optimizes the overall performance of the model through a multi-task learning framework. Experimental results show that Y-MaskNet achieves a significant improvement in detection and segmentation tasks, with mAP@[0.5:0.95] reaching 0.72 (up from 0.62 for YOLOv5 and 0.65 for Mask R-CNN) on the PCB Defect Dataset, and IoU improving by 7% compared to existing methods. These improvements are particularly notable in small object detection and fine-grained defect segmentation, making Y-MaskNet an efficient and accurate solution for defect detection in electronic products, offering strong technical support for future industrial intelligent quality control.