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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2019, Vol. 53 Issue (10): 1883-1891    DOI: 10.3785/j.issn.1008-973X.2019.10.005
Mechanical and Energy Engineering     
Visual salience detection algorithm for surface defects of friction sheets
Zhong-wei QIN1(),Jie CHEN1,*(),Rong-jing HONG1,Wei-wei WU2
1. School of Mechanical and Power Engineering, Nanjing Tech University, Nanjing 211800, China
2. School of Mechanical, Yangzhou University, Yangzhou 225009, China
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Abstract  

A detection algorithm based on visual salience was proposed according to the requirement of high precision and efficiency for detecting the surface defect of friction sheet and complex surface conditions of the friction sheets themselves. The friction sheets were separated from background by the image segmentation. The surface texture was smoothed by Gaussian Blur. The multi-scale detail enhancement algorithm was used to compensate missing defect edge information in Gaussian Blur, and the saliency of the target in this image was calculated for differentiation. The connected domain method and Otsu were utilized to extract the binary images of the defect area. The experimental results show that the algorithm has strong pertinence for the defect detection of friction sheets. The defect recognition rate is over 98%. It takes 27 s to detect 100 friction sheets on both sides. From objective and subjective aspects, the detection results prove that the algorithm has high recognition rate and accuracy to meet the demand of industrial assembly.

Key words： friction sheet      defect detection      Gaussian Blur      detail enhancement      saliency     
Received: 22 August 2018      Published: 30 September 2019
CLC:  TP 391  
Corresponding Authors: Jie CHEN     E-mail: 2553230346@qq.com;article_1971@163.com.cn
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Zhong-wei QIN
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Wei-wei WU
Cite this article:

Zhong-wei QIN,Jie CHEN,Rong-jing HONG,Wei-wei WU. Visual salience detection algorithm for surface defects of friction sheets. Journal of ZheJiang University (Engineering Science), 2019, 53(10): 1883-1891.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2019.10.005     OR     http://www.zjujournals.com/eng/Y2019/V53/I10/1883


摩擦片表面缺陷的视觉显著性检测算法

针对摩擦片表面缺陷高精度高效率的检测要求以及摩擦片自身复杂的表面状况，提出基于视觉显著性的检测算法. 利用图像分割，将摩擦片从背景中分离；使用高斯平滑弱化表面纹理，采用多尺度细节增强算法补偿高斯平滑中丢失的缺陷边缘信息，计算图像中目标的显著性进行强弱分化；采用连通域法和OTSU，提取缺陷区域的二值图像. 经由实验验证，该算法针对摩擦片的缺陷检测具有较强的针对性，缺陷识别率超过98%，双面检测100个摩擦片用时27 s. 从客观和主观两个方面对检测结果进行评价验证，结果表明，该算法具有较高的识别率和精确度，满足工业检测的需求.


关键词： 摩擦片,  缺陷检测,  高斯平滑,  细节增强,  显著性 
Fig.1 Composition of defect detection system
Fig.2 Extraction of undetected area in friction sheets
Fig.3 Surface feature distribution of friction sheets
Fig.4 Impact on image quality from window size and standard deviation of filter
Fig.5 Comparison of enhancing image details
Fig.6 Distribution in three-dimensional space of defect salience value
Fig.7 Comparison of gray value distribution between original image and saliency image
Fig.8 Binary diagram of defection in friction sheets
Fig.9 Algorithm detection results compared with human-annotated model of defect areas
样本类型 Nnum RY RN RW RO PAC/%
划痕 30 30 0 0 0 100
龟裂 30 30 0 0 0 100
油污 20 19 1 0 1 95
无缺陷 20 0 20 0 0 100
有缺陷
（纹理检测） 		80 80 0 0 0 100
无缺陷
(纹理检测) 		20 17 3 17 0 85
有缺陷（差分） 80 71 9 0 9 89
无缺陷（差分） 20 12 8 12 0 60
Tab.1 Comparison of qualitative analysis results between this algorithm and other similar algorithms
缺陷类型 P R F
划痕 0.890 1 0.784 2 0.833 8
龟裂 0.726 3 0.900 1 0.803 9
油污 0.953 1 0.630 5 0.758 9
划痕[15] 0.864 2 0.742 1 0.798 5
龟裂[15] 0.721 7 0.768 5 0.744 4
油污[15] 0.437 6 0.651 4 0.523 5
Tab.2 Comparison of quantitative analysis results between this algorithm and other similar algorithms
Fig.10 Salience value distribution chart of shallow oil defects
方法 tsum/s PAC/%
本算法 27 99
算法A 33 91
算法B 94 83
Tab.3 Comparison of detection efficiency between this algorithm and other similar algorithms
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