• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2016, Vol. 38 ›› Issue (03): 549-555.

• 论文 • 上一篇    下一篇

基于视觉显著度的皮革表面瑕疵检测

朱凌云,严飞华,李汶松   

  1. (重庆理工大学计算机科学与工程学院,重庆 400054)
  • 收稿日期:2015-05-14 修回日期:2015-07-26 出版日期:2016-03-25 发布日期:2016-03-25
  • 基金资助:

    国家自然科学基金(61502064);重庆理工大学研究生创新基金(YCX2014228)

Leather surface defect detection
based on visual saliency degree

ZHU Lingyun,YAN Feihua,LI Wensong   

  1. (College of Computer Science and Engineering,Chongqing University of Technology,Chongqing 400054,China)
  • Received:2015-05-14 Revised:2015-07-26 Online:2016-03-25 Published:2016-03-25

摘要:

针对汽车内饰皮革的瑕疵检测易受皮革自身纹理干扰、检测难度较大的问题,发现瑕疵存在于均匀变化图像中局部变化明显的区域,符合人眼注意机制,故提出了基于视觉显著模型的皮革瑕疵检测方法。首先提取皮革图像的颜色和亮度特征,然后利用中心周围差算子分别计算特征显著图,再融合成最终显著图,最后在此基础上利用区域生长方法对瑕疵区域进行分割,以实现瑕疵的准确定位。实验结果表明,与FCM聚类分割法、阈值分割法及SVM分类法相比,本文提出的方法具有较高的检测精度及较快的检测速度,解决了皮革瑕疵检测过程中受纹理干扰严重等问题,能有效应用于皮革瑕疵的机器自动检测中。

关键词: 皮革, 瑕疵检测, 显著图, 分割

Abstract:

Leather defect inspection is seriously influenced by its texture. In view that finding the defects existing in the local highlight areas of uniform change images conforms to human eye attention mechanism, we propose an automatic method for detecting leather defects based on the visual saliency model. Firstly, the features of color and brightness of the leather’s image are extracted, and then their own characteristic maps are calculated respectively by centersurround operator. Then the final saliency map is formed through merging. On this basis, the region growing method is adopted to locate the flaw detection area. In comparison with the FCM clustering segmentation, the threshold segmentation and the SVM classification, the experimental results show that the proposed method has higher precision and faster detection speed, and it can solve the problems existing in the process of leather defect detection which can be seriously affected by its texture, and be applied to automatic machine detection of leather defects.  

Key words: leather;flaw detection;saliency map;segmentation