无线电工程2024，Vol.54Issue(1)：55-62,8.DOI:10.3969/j.issn.1003-3106.2024.01.008
基于感知哈希算法的特征融合玻璃瓶缺陷检测
Feature Fusion Glass Bottle Defect Detection Based on Perceptual Hash Algorithm
摘要
Abstract
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%.关键词
缺陷检测/方向梯度直方图特征/SIFT特征/支持向量机/感知哈希算法Key words
defect detection/HOG features/SIFT features/SVM/PHA分类
信息技术与安全科学引用本文复制引用
傅莉,吉宏轩,张宇峰,任艳..基于感知哈希算法的特征融合玻璃瓶缺陷检测[J].无线电工程,2024,54(1):55-62,8.基金项目
国家自然科学基金(61602321)National Natural Science Foundation of China(61602321) （61602321）
