Joining Spatial Deformable Convolution and a Dense Feature Pyramid for Surface Defect Detection

Although deep learning-based surface defect detection approaches have performed remarkably well in recent years, the complicated shapes and large size differences of surface defects still pose enormous challenges for most existing methods. To address these issues, we propose a novel surface defect detection method joining spatial deformable convolution and a dense feature pyramid, named SDDF-Net. First, we construct a spatial deformable convolution-based feature extraction network, which uses a dynamic convolutional kernel with spatial information to increase the feature extraction capability of complicated defects. Second, we build a dense feature pyramid-based feature fusion network that fuses features from different network layers to improve the detection accuracy of multiscale defects. Third, we present a novel hybrid loss that combines complete intersection over union loss and normalized Wasserstein distance loss to enhance the defect recognition and location learning abilities of our method. Finally, we run our method on the NEU-DET, DAGM2007 and DeepPCB datasets to conduct a comprehensive comparison with some state-of-the-art general object detection models and specialized surface defect detection methods. The experimental results show that the proposed SDDF-Net performs competitively in terms of detection accuracy and computational efficiency when compared with existing methods. This indicates that SDDF-Net achieves good results for surface defect detection and is qualified for real-time processing in industry.