Surface Defect Detection via Entity Sparsity Pursuit with Intrinsic Priors

Computer vision based methods have been widely used in surface defect inspection. However, most of these approaches are task-specific, and it is hard to transfer them to similar detection scenarios. This paper proposes an entity sparsity pursuit (ESP) method to identify surface defects. Based on the observation that surface image textures usually form a low-rank structure and the structure can be violated by the presence of rare defects, we formulate the detection task as a low-rank and entity sparsity pursuit problem. To alleviate the feature shortage issue existed in industrial grayscale images, we customize a kind of intuitive features for surface defect inspection. Different from previous work utilizing complicated regularization terms, we resort to mine intrinsic priors of defect images, which can be neatly incorporated into the designed architecture. The proposed model is compact and able to detect surface defects in an unsupervised manner. To fully evaluate the presented method, we conduct a series of experiments using two real-world and one synthetic defect datasets. Experimental results demonstrate that ESP outperforms state-of-the-art methods.