基于感知哈希算法的特征融合玻璃瓶缺陷检测OA

Feature extraction is a crucial step in glass bottle defect detection task.The rich feature information in feature set will directly affect the accuracy of defect detection.However,the feature information extracted by the traditional single feature extraction algorithm is often too simple,leading to a low accuracy of the final detection.To solve these problems,a feature extraction algorithm based on the fusion of Histogram of Oriented Gradients(HOG)feature and Scale Invarient Feature Transform(SIFT)feature is proposed.To address the problem that contour extraction from different defect edges is not accurate enough,an edge detection operator selection method based on Perceptual Hash Algorithm(PHA)is proposed.Support Vector Machine(SVM)is used for training and verification.Experimental results show that the edge detection operator selection method proposed can select the most suitable edge detection operator for different defects,and the average accuracy of the feature fusion algorithm can reach88.7%.Compared with the single HOG feature extraction algorithm,it is improved by7.99%,and compared with the single SIFT feature extraction algorithm,it is improved by 2.97%.