DEPANet: A Differentiable Edge-Guided Pyramid Aggregation Network for Strip Steel Surface Defect Segmentation

The steel strip is an important and ideal material for the automotive and aerospace industries due to its superior machinability, cost efficiency, and flexibility. However, surface defects such as inclusions, spots, and scratches can significantly impact product performance and durability. Accurately identifying these defects remains challenging due to the complex texture structures and subtle variations in the material. In order to tackle this challenge, we propose a Differentiable Edge-guided Pyramid Aggregation Network (DEPANet) to utilize edge information for improving segmentation performance. DEPANet adopts an end-to-end encoder-decoder framework, where the encoder consisting of three key components: a backbone network, a Differentiable Edge Feature Pyramid network (DEFP), and Edge-aware Feature Aggregation Modules (EFAMs). The backbone network is designed to extract overall features from the strip steel surface, while the proposed DEFP utilizes learnable Laplacian operators to extract multiscale edge information of defects across scales. In addition, the proposed EFAMs aggregate the overall features generating from the backbone and the edge information obtained from DEFP using the Convolutional Block Attention Module (CBAM), which combines channel attention and spatial attention mechanisms, to enhance feature expression. Finally, through the decoder, implemented as a Feature Pyramid Network (FPN), the multiscale edge-enhanced features are progressively upsampled and fused to reconstruct high-resolution segmentation maps, enabling precise defect localization and robust handling of defects across various sizes and shapes. DEPANet demonstrates superior segmentation accuracy, edge preservation, and feature representation on the SD-saliency-900 dataset, outperforming other state-of-the-art methods and delivering more precise and reliable defect segmentation.