A convolutional neural network-based method for workpiece surface defect detection

Abstract

The surface defects of the workpiece affect the workpiece quality. In order to detect workpiece surface defects more accurately, an automatic detection convolutional neural networks-based method is proposed in this paper. Firstly, a convolution network classification model (SCN) with symmetric modules is proposed, which is used as backbone of our method to extract features. And then, three convolution branches with FPN structure are used to identify the features. Finally, an optimized IOU (XIoU) is designed to define the loss function, which is used for detection model training. In addition to the public datasets NEU-CLS and NEU-DET, a classification dataset and a detection dataset of surface defects on hearth of raw aluminum casting are established to train and evaluate our model. On the basis above, the proposed backbone SCN was compared with Darknet-53 and ResNet-101 to present its superiority in classification performance. The average accuracy of SCN on NEU-CLS and self-made data sets are 99.61% and 95.84% respectively, which is significantly higher than the other two classification models. Then, in order to show the effectiveness and superiority of the proposed automatic detection method, the detection performance of the method is compared with the Faster-RCNN series and the YOLOv3 series. The result shows that our model achieves 79.89% mAP on NEU-DET and 78.44% mAP on self-made detection dataset. Our model can detect at 23f/s when the input image size is 416 × 416 × 3. The detection performance of our model is significantly better than other models. The results show that the proposed method has better performance and can be used for real-time automatic detection of workpiece surface defects.