Abstract
Surface defect detection of an automobile wheel hub is important to the automobile industry because these defects directly affect the safety and appearance of automobiles. At present, surface defect detection networks based on convolutional neural network use many pooling layers when extracting features, reducing the spatial resolution of features and preventing the accurate detection of the boundary of defects. On the basis of DeepLab v3+, we propose a semantic segmentation network for the surface defect detection of an automobile wheel hub. To solve the gridding effect of atrous convolution, the high-resolution network (HRNet) is used as the backbone network to extract high-resolution features, and the multi-scale features extracted by the Atrous Spatial Pyramid Pooling (ASPP) of DeepLab v3+ are superimposed. On the basis of the optical flow, we decouple the body and edge features of the defects to accurately detect the boundary of defects. Furthermore, in the upsampling process, a decoder can accurately obtain detection results by fusing the body, edge, and multi-scale features. We use supervised training to optimize these features. Experimental results on four defect datasets (i.e., wheels, magnetic tiles, fabrics, and welds) show that the proposed network has better F1 score, average precision, and intersection over union than SegNet, Unet, and DeepLab v3+, proving that the proposed network is effective for different defect detection scenarios.
Highlights
Common defects of automobile wheel hub include fray, hole, nick, and spot
The detect maps of SegNet and DeepLab v3+, have some white dots outside marked by white boxes, indicating that SegNet and DeepLab v3+ are overfitting and some normal pixels are detected as defect pixels
Atrous Spatial Pyramid Pooling (ASPP) based on feature superposition design avoids the gridding effect of atrous convolution
Summary
Common defects of automobile wheel hub include fray, hole, nick, and spot. These defects seriously affect the safety and appearance of automobiles [1]. Ineffective and inaccurate manual detection greatly restricts the production level of the wheel manufacturing industry. The surface defect detection of automobile wheel hubs is manual. An intelligent surface defect detection method for automobile wheel hubs must be urgently designed for the wheel manufacturing industry. According to the input image, the intelligent surface defect detection method aims to predict defects and their locations
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