Abstract
Surface defect segmentation supports real-time surface defect detection system of steel sheet by reducing redundant information and highlighting the critical defect regions for high-level image understanding. Existing defect segmentation methods usually lack adaptiveness to different shape, size and scale of the defect object. Based on the observation that the defective area can be regarded as the salient part of image, a saliency detection model using double low-rank and sparse decomposition (DLRSD) is proposed for surface defect segmentation. The proposed method adopts a low-rank assumption which characterizes the defective sub-regions and defect-free background sub-regions respectively. In addition, DLRSD model uses sparse constrains for background sub-regions so as to improve the robustness to noise and uneven illumination simultaneously. Then the Laplacian regularization among spatially adjacent sub-regions is incorporated into the DLRSD model in order to uniformly highlight the defect object. Our proposed DLRSD-based segmentation method consists of three steps: firstly, using DLRSD model to obtain the defect foreground image; then, enhancing the foreground image to establish the good foundation for segmentation; finally, the Otsu’s method is used to choose an optimal threshold automatically for segmentation. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both subjective and objective tests. Meanwhile, the proposed method is applicable to industrial detection with limited computational resources.
Highlights
Surface defect detection plays an important role in quality enhancement in industrial product manufacturing
Our proposed method is compared with eight representative saliency detection methods quantitatively and qualitatively, such as RPCA [28], IS [13], ULR [23], RBD [31], SBD [32], DSR [33], RS [16] and SMF [24], where RPCA, IS, ULR, RBD, SBD, DSR, RS and SMF represent the method of robust principal component analysis, image signature, unified low rank matrix recovery, robust background detection, spaces of background-based distribution, dense and sparse reconstruction, ranking saliency and structured matrix decomposition, respectively
Indicates that the negative pixel is judged as the positive pixel mistakenly; precision = true positive (TP)/( TP + false positive (FP)), recall = TP/( TP + false negative (FN) ); N represents the number of surface defect image samples of the same class, H and W denotes the height and width of surface defect image, respectively; precision is defined as the percentage of defect pixels correctly assigned, while recall is the ratio of correctly detected defect pixels to all true defect pixels
Summary
Surface defect detection plays an important role in quality enhancement in industrial product manufacturing. Most recently,with withthe thedevelopment development saliency detection technology, segmentation methods use saliency map are gradually rising in the industrial defect inspection field. Proposed saliency map construction method which provide the good foundation for segmentation. SegmentationThen is conducted with is the saliency map This model exhibits good performance for strip steel defectfor detection. This model exhibits good performance strip steel detection. Textile fabric defect presented a novel saliency detection model, which obviously improve the accuracy of automated. As the surface defect image of steel sheet has a low signal-to-noise disturbance. DLRSD-based segmentation method for the surface results demonstrate the feasibility effectiveness of the proposed defect steel the of same provides antime, interesting perspective for theperspective industrial methodoffor the sheet.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
