X-ray detection of ceramic packaging chip solder defects based on improved YOLOv5

It is of great significance to detect solder defects of ceramic packaging chips by X-ray detection to improve the quality of electronic products. However, the scale of the solder defects varies violently and there are a lot of tiny, dense, and long narrow defects, making it difficult to identify timely and accurately. To solve the issues mentioned above, an improved object detection model based on the YOLOv5 network, namely YOLO-STPN, is proposed in this paper. We add another prediction head to the original model to detect tiny solder defects effectively. In addition, the swin transformer block and convolutional block attention module are integrated into the path aggregation network, which improve the networks' ability to identify dense and long narrow defects. The positive sample matching mechanism and the complete intersection over union (CIoU) loss are modified to make the model focus on long narrow defects. Experimental results of 10 types of ceramic packaging chips show that the mean average precision at 50 % IoU (mAP50) of YOLO-STPN is 10.05 % higher than the original YOLOv5 on average, while the decrease of frames per second (FPS) is acceptable. The proposed method has high accuracy and speed. Therefore, it is applicable to the real-time detection of chip packaging.