Dense Attention-Guided Cascaded Network for Salient Object Detection of Strip Steel Surface Defects

Recently, more and more researchers have paid attention to the surface defect detection of strip steel. However, the performance of existing methods usually fails to detect the defect regions from some complex scenes, especially with the noise disturbance and diverse defect types. Therefore, this article proposes an end-to-end dense attention-guided cascaded network (DACNet) to detect salient objects (i.e., defects) on the strip steel surface, where the proposed DACNet is a U-shape network including an encoder and a decoder. The encoder first deploys multiresolution convolutional branches (i.e., high/medium/low) in a cascaded way. Concretely, the cascaded feature integration (CFI) unit fuses the deep features from the last convolutional blocks of multiresolution branches, yielding the enhanced high-level deep semantic feature. Subsequently, coupled with the multilevel deep features from high-resolution branch, the new multiscale deep features are capable of characterizing various defects. Then, driven by the dense attention mechanism which enables the deeper attention cues flow into decoding stages, the decoder progressively integrates the multiscale deep features into the final saliency map, where the dense attention is designed to steer deep features pay more concerns to the defect regions. Comprehensive experiments are conducted on the public strip steel datasets, and the experimental results demonstrate that our model consistently outperforms the state-of-the-art models in all evaluation metrics.