Abstract
The detection of surface defects is very important for the quality improvement of steel plates. In actual production, as the steel plate production line runs faster, the steel surface defect detection algorithm is required to meet the requirements of real-time detection (less than 100 ms/image), and the detection accuracy is improved (at least 90%). In this paper, an improved multi-block local binary pattern (LBP) algorithm is proposed. This algorithm not only has the simplicity and efficiency of the LBP algorithm, but also finds a suitable scale to describe the defect features by changing the block sizes, thus ensuring high recognition accuracy. The experiment proves that the method satisfies the requirements of online real-time detection in terms of speed (63 ms/image), and surpasses the widely-used scale invariant feature transform (SIFT), speeded up robust features (SURF), gray-level co-occurrence matrix (GLCM), and LBP algorithms in recognition accuracy (94.30%), which prove that the MB-LBP has practical application value in an online real-time detection system.
Highlights
Steel plates are widely used in engineering fields such as ships, bridges, machinery, construction, and automobile manufacturing
The surface defect recognition algorithm for steel plates is the core part of the entire surface defect detection system
In we proposed proposedaasurface surfacedefect defectdetection detection algorithm steel plates, which adopted algorithm to extract the defect features
Summary
Steel plates are widely used in engineering fields such as ships, bridges, machinery, construction, and automobile manufacturing. Proposed a robust on multi-scale element, which can filter the noise effectively, but can delete the false detection method based on the vision and deep learning of feature mapbased in order characteristics of the cracks, scales, andattention slag. Di et al [9] proposed a new semi-supervised learning method supervised generative adversarial networks to classify thegenerative surface defects of steels. Jiang et al.defect [10] suggested detecting the network This method divides the casting into multiple regions, preprocesses the image of each appearance defect of castings based on a deep residual network. Production, the steel production line runs very fast, so the surface defect detection algorithm needs to paper, requirements we propose the multi-block binary pattern (MB-LBP) algorithm to extract the meet In thethis real-time while ensuringlocal the accuracy.
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