哈尔滨商业大学学报（自然科学版）2025，Vol.41Issue(2)：152-160,9.
自监督结构化重建的工业图像异常检测与定位
Structural reconstruction of industrial image anomaly detection and localization based on self supervised learning
摘要
Abstract
To solve the issues of insufficient defect training samples and high missing rates in the surface anomaly detection of industrial images,a self-supervised structural reconstruction-based anomaly detection and localization model was designed.The model could accurately detect and locate abnormal regions using only normal sample training.The model reconstructed the input image based on an autoencoder.Due to its strong generalization performance,the abnormal region was also well reconstructed,leading to a reduction in thereconstruction error of the abnormal region between the input image and the reconstructed image,and thus decreasing the anomaly detection accuracy.Themulti-complementary mask fusion method was proposed to reduce the likelihood of reconstructing the abnormal region while ensuring that the normal region was well reconstructed,thereby improving the accuracy of anomaly reconstruction error identification.Anomaly detection and localization were achieved by evaluating the multi-scale structural similarity loss between the input image and the fused reconstructed image.Experimental results showed that the anomaly detection rate of the proposed method was 1.6%and 3.4%higher than that of the other two advanced methods on the MVTec AD dataset under the image-level and pixel-level AUC standards,respectively.The abnormal localization rate was increased by 1.7%and 4.5%,respectively,demonstrating more effective detection performance.关键词
自监督/异常检测与定位/结构化重建/掩码重构/融合算法/结构相似性损失Key words
self supervised learning/anomaly detection and localization/structured reconstruction/mask reconstruction/fusion algorithm/structural similarity loss分类
计算机与自动化引用本文复制引用
范伟平,程凡永,张明艳..自监督结构化重建的工业图像异常检测与定位[J].哈尔滨商业大学学报（自然科学版）,2025,41(2):152-160,9.基金项目
国家自然科学基金面项目(61976005) （61976005）
芜湖市重点研发项目(2022yf42) （2022yf42）
检测技术与节能装置安徽省重点实验室开放研究基金(JCKJ2021B06) （JCKJ2021B06）
安徽工程大学-鸠江区产业协同创新项目(2022cyxtb10) （2022cyxtb10）
