Abstract
The performance of the salient object detection of strip surface defects has been promoted largely by deep learning based models. However, due to the complexity of strip surface defects, the existing models perform poorly in the challenging scenes such as noise disturbance, and low contrast between defect regions and background. Meanwhile, the detection results of existing models often suffer from coarse boundary details. Therefore, we propose a novel saliency model, namely an Edge-aware Multi-level Interactive Network, to detect the defects from the strip steel surface. Concretely, our model adopts the U-shape architecture where the two crucial points are the interactive feature integration and the edge-guided saliency fusion. Firstly, except the skip connection that combines the same stage of encoder and decoder, we deploy another connection, where the features from adjacent levels of encoder are transferred to the same stage of decoder. By this way, we are able to provide an effective fusion of multi-level deep features, yielding a well depiction for defects. Secondly, to give well-defined boundaries for prediction results, we add the edge extraction branch after each decoder block, where the progressive feature aggregation endows the edge with precise details and complete object cues. Meanwhile, together with the edge extraction branches, we deploy the saliency prediction branch at each decoder stage. After that, coupled with the fine edge information, we fuse all outputs of saliency prediction branches into the final saliency map, where the edge cue steers the saliency result to pay more attention to the boundary details. Following this way, we can provide a high-quality saliency map which can accurately locate and segment the defects. Extensive experiments are performed on the public dataset, and the results prove the effectiveness and robustness of our model which consistently outperforms the state-of-the-art models.
Highlights
S URFACE defect detection is a very important research area in the field of machine vision, which tries to locate the defect regions in the collected surface images
To address the above challenges, we propose a novel saliency model, namely Edge-aware Multi-level Interactive Network shown in Fig. 1, to detect the strip steel surface defects
We focus on the interaction of features from different layers and the effect of edge information, which gives an effective boost for the performance of salient object detection
Summary
S URFACE defect detection is a very important research area in the field of machine vision, which tries to locate the defect regions in the collected surface images. The traditional saliency models are mainly designed based on the handcrafted features which cannot give a well depiction for strip steel surface defects, especially some complex surface scenes such as low contrast between defects and backgrounds, small defect regions, and noise disturbance This often results in the generated saliency map cannot pop-out the defects completely. To address the above challenges, we propose a novel saliency model, namely Edge-aware Multi-level Interactive Network shown, to detect the strip steel surface defects. The contribution of this paper can be summarized as follows: 1) We propose a novel saliency model, i.e. Edge-aware Multi-level Interactive Network, to detect strip steel surface defects, where the two key points are the interactive feature integration and the edge-guided saliency fusion. To present high-quality boundary details of defect regions, we introduce the edge information to refine the saliency fusion
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