Development of a cross-scale weighted feature fusion network for hot-rolled steel surface defect detection

Surface defects of hot-rolled steel would affect the performance and appearance of the final products. In order to detect steel surface defects efficiently, a cross-scale weighted feature fusion network for identifying defect categories and locating defects is proposed in this work. Combined with Laplace sharpening, the backbone in the YOLOv5s model is used to extract multi-scale defect features from input images. And then, an improved weighted bi-directional feature pyramid network embedded with residual modules is proposed to aggregate multi-scale feature maps for enhancing the robustness of multi-size defect representation. Finally, four prediction branches accompanied with prior bounding boxes by a k-means clustering algorithm are responsible for predicting defects with different sizes. The proposed detection network is verified on the NEU-DET dataset, and experimental results show that the proposed network can achieve 86.8% mAP with the IoU threshold of 0.5, and can efficiently process images at 51 fps with the RGB image size 640 x 640. The Laplace sharpening module, the k_means clustering module and the improved C3-BiFPN module all contribute to the improvement of performance (mAP) of the proposed network by 1.8%, 2.7% and 3.8%, respectively. Our experimental results demonstrate that the proposed framework can effectively detect the surface defects of hot-rolled steel, and has potential to be used for real-time surface defect detection. Meanwhile, the versatility of the proposed network for other types of defect detection is also evaluated on the MT dataset and the DAGM dataset.