Research on detection algorithm of solder joint wetting defect of CQFP package devices

To tackle the issue of dense IC components that complicate the detection of wetting defects in pin solder joints of CQFP package devices, we propose an improved YOLOv5 algorithm, named YOLO-HP, specifically designed for detecting post-wetting defects in pin solder joints. Firstly, due to the high resolution of the images, an x-large detection layer is added on top of the existing three detection layers to enhance the detection of pin root information. Second, a hybrid pooled attention module is designed to capture both global and local feature map information using multiple pooling methods, thereby improving object detection performance. Finally, the Context Aggregation Block is introduced to aggregate contextual information through attention mechanisms, enhancing the model's perception of the object region. The improved algorithm achieves an average accuracy ([email protected]) of 96.9% on a custom pin wetting defect dataset, which is 6.5 percentage points higher than the original YOLOv5s baseline network. This improvement meets the needs of solder joint wetting defect detection and has practical value for future IC soldering defect detection.