This paper proposed a solder joint and wire bonding segmentation approach for detecting the bonding effect of RF chip based on AOI. According to the characteristics of solder joint and wire bonding segmentation task, this method improves the prior frame generation mechanism of the feature pyramid layer in Mask R-CNN. Also, it introduces a data enhancement method based on collision detection, which reduces manual annotation cost. The results show that the improved Mask R-CNN model can obtain the accurate segmentation positions of the solder joint and wire bonding in RF chips with mAP of 85.23% and mIoU of 71.27%. Meanwhile, this method speeds up to 1.168 per image, which basically meets the requirements for RF chip speed in production. Overall, the proposed method achieves high segmentation accuracy and meets the industrial production requirements for timeliness to a certain extent in the solder joint and wire bonding segmentation task.