Aiming at the low accuracy and low efficiency of manual defect detection of stamping aluminum sheet, a surface defect detection based on modified PHOG algorithm is proposed in this paper. PHOG is a spatial shape descriptor that introduces spatial pyramid model to achieve multi-scale representation of the target. However, in the field of target recognition with similar shapes and different details, the PHOG lacks a description of the relationship between pixels in the image. In order to improve the detail description ability of PHOG algorithm, the sliding window mechanism is introduced to improve PHOG. Based on the improved PHOG, combined with the advantages of SVM, the defect detection algorithm flow is given. The experimental results show that, the proposed defect detection algorithm has good accuracy and real-time performance.
Fabric defect detection is an important part of quality control in textile producing enterprises. In order to effectively improve the detection probability, the fabric defects detection algorithm based on multifractal spectrum (MFS) and support vector machine (SVM) is proposed in this paper. The detection process is divided into two main parts: feature extraction and classification, including image segmentation, MFS feature extraction, SVM model training, detection classification and classification results. The simulation experiment results show that the algorithm has good performance of detection and classification based on the detection rate and the false alarm rate, and it has a certain robustness and can be applied to the actual generation.
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