华中科技大学学报（自然科学版）2024，Vol.52Issue(6)：39-47,9.DOI:10.13245/j.hust.240599
基于显著性检测的坯布疵点图像自适应分割方法
Adaptive segmentation method of grey fabric defect image based on saliency detection
摘要
Abstract
To address the issue of small defects blending into the fabric background due to the irregular shapes and the difficulty of effective edge segmentation,an adaptive segmentation method of grey fabric defect image was proposed based on saliency detection.This approach utilized visual saliency techniques to analyze light intensity distribution,shape and color difference characteristics of defects on grey fabric images,and a multi-visual saliency feature function was designed to label salient areas in super-pixels.Then,a method of calculating the adaptive segmentation threshold was proposed to extract the defect boundary details,and thereby segmenting and localizing defect regions on grey fabric images were carried on.Finally,the accurate detection of defects was realized.Experimental results show that the proposed approach can obtain uniform and dense defect areas with clear boundaries,and the overall detection accuracy of 6 types of defects reaches 98.26%,which can effectively improve the results of grey fabric defect image segmentation.关键词
坯布疵点检测/视觉显著性/特征提取/图像分割/自适应阈值Key words
grey fabric defects detection/visual saliency/feature extraction/image segmentation/adaptive threshold分类
信息技术与安全科学引用本文复制引用
朱子洵,张洁,汪俊亮..基于显著性检测的坯布疵点图像自适应分割方法[J].华中科技大学学报（自然科学版）,2024,52(6):39-47,9.基金项目
国家自然科学基金资助项目(52375485) （52375485）
中央高校基本科研业务费专项资金资助项目(2232024G-14) （2232024G-14）
上海市自然科学基金资助项目(22ZR1403000). （22ZR1403000）
