Abstract: In order to better detect the defects of the mobile phone data interface, this paper proposes a detection algorithm based on Faster R-CNN. The specific research method is to replace RoIPooling in the Faster R-CNN detection architecture with RoIAlign to solve the deviation of the target return position caused by the two quantifications in the RoIPooling calculation process. The ResNet50 fusion FPN network is used as a feature extraction network to improve the model’s detection effect on small target defects. Finally, the test set is used for prediction. Experiments show that the mean average accuracy (mAP) of the proposed algorithm in this paper has reached 91.17%, which is 24.72 percent points higher than mAP when VGG is used as the feature extraction network, and is 2.58 percent points higher than mAP when ResNet50 is used alone as the feature extraction network. Therefore, the algorithm proposed in this paper has a significant effect on mobile phone data interface defect detection, and provides a more effective detection method for mobile phone data interface defect detection.

Key words: deep learning, residual networks, feature extraction, mobile phone data interface defect detection, feature pyramid networks