End-to-end aluminum strip surface defects detection and recognition method based on ViBe
Gang YE1(),Yi-bo LI1,2,3,*(),Zhu-xi MA2,Jie CHENG1
1. Light Alloy Research Institute, Central South University, Changsha 410083, China 2. School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China 3. State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha 410083, China
An end-to-end surface defects detection and recognition method was proposed to solve the problem of high-precision detection of aluminum strip surface defects and the poor recognition rate of traditional algorithms. The average image was quickly calculated from the initial image sequence of aluminum strip surface, which was regarded as defect-free background image and was used to initialize the background model of the ViBe algorithm. The ViBe algorithm was used to segment the defect region from the current image. Median filtering and morphological operation were performed on the binary image of defect region to remove noise points and repair edges in order to accurately extract the defect region. The current image was used to update the ViBe background model in real time in order to increase the adaptability of the algorithm to illumination changes. The image of external rectangular region of the defect was extracted, normalized, and input into the trained convolutional neural networks for recognition and classification. The classification result was obtained. The experimental results show that the proposed method has a defect detection rate of 93.02% and a defect recognition rate of 99.86%, which has good application value.
Gang YE,Yi-bo LI,Zhu-xi MA,Jie CHENG. End-to-end aluminum strip surface defects detection and recognition method based on ViBe. Journal of ZheJiang University (Engineering Science), 2020, 54(10): 1906-1914.
Fig.2Flow chart of defects detection and recognition algorithm
Fig.3Structure of defects recognition model based on VGG16
Fig.4Image acquisition platform
Fig.5Sample distribution of defects dataset
类别
黑点
凹凸包
凹坑
脏污
划痕
缺陷原图
分割结果
缺陷轮廓
缺陷外接矩形
Tab.1Examples of defects detection results
Fig.6Comparison of segmentation effects of different algorithms
Fig.7Accuracy curve and loss curve during CNN training
缺陷类别
黑点
凹凸包
凹坑
脏污
划痕
Pacc / %
黑点
98
0
0
1
0
98.99
凹凸包
0
212
0
0
0
100
凹坑
0
0
182
0
0
100
脏污
0
0
0
92
0
100
划痕
0
0
0
0
114
100
Tab.2Confusion matrix of defects classification experimental results
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