深度学习在手机数据接口缺陷检测中的应用

为了能更好地对手机数据接口的缺陷进行检测,提出一种基于Faster R-CNN的检测算法.将Faster R-CNN检测架构中的RoIPooling替换成RoIAlign,解决RoIPooling计算过程中2次量化造成的目标回归位置的偏差.使用ResNet50融合FPN的网络作为特征提取网络,提高模型对小型目标缺陷的检...

Application of Deep Learning in Defect Detection of Mobile Phone Data Interface

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.