Please wait a minute...
Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (10): 1906-1914    DOI: 10.3785/j.issn.1008-973X.2020.10.006
    
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
Download: HTML     PDF(1825KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

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.



Key wordssurface defects of aluminum strip      defects detection      defects recognition      ViBe      convolutional neural network (CNN)     
Received: 11 December 2019      Published: 28 October 2020
CLC:  TP 391  
Corresponding Authors: Yi-bo LI     E-mail: yeg2020@163.com;yibo.li@csu.edu.cn
Cite this article:

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.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.10.006     OR     http://www.zjujournals.com/eng/Y2020/V54/I10/1906


基于ViBe的端到端铝带表面缺陷检测识别方法

针对铝带表面缺陷高精度检测要求以及传统算法识别率不佳的问题,提出端到端的表面缺陷检测与识别方法.从铝带表面初始图像序列中快速计算出平均图像,视为无缺陷背景图像,用于初始化ViBe算法的背景模型.采用ViBe算法从当前图像中分割出缺陷区域,对缺陷区域二值图像进行中值滤波和形态学运算,以去除噪声点和修补边缘,实现缺陷区域的准确提取.利用当前图像实时更新ViBe背景模型,以增加对光照变化的适应能力.提取缺陷外接矩形区域图像,归一化后输入到训练好的卷积神经网络中进行识别分类,得到分类结果.实验结果表明,提出方法的缺陷检出率为93.02%,缺陷识别率为99.86%,具有较好的应用价值.


关键词: 铝带表面缺陷,  缺陷检测,  缺陷识别,  ViBe,  卷积神经网络(CNN) 
Fig.1 Discriminant model of ViBe background points
Fig.2 Flow chart of defects detection and recognition algorithm
Fig.3 Structure of defects recognition model based on VGG16
Fig.4 Image acquisition platform
Fig.5 Sample 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.1 Examples of defects detection results
Fig.6 Comparison of segmentation effects of different algorithms
Fig.7 Accuracy 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.2 Confusion matrix of defects classification experimental results
[1]   康军伟, 张建辉, 曾宏凯 国内外铝加工行业状况浅析[J]. 有色金属加工, 2017, 46 (6): 1- 5
KANG Jun-wei, ZHANG Jian-hui, ZENG Hong-kai Analysis of aluminum processing industry at home and abroad[J]. Nonferrous Metals Processing, 2017, 46 (6): 1- 5
[2]   陈良 国内铝板带箔加工行业的现状分析[J]. 有色金属加工, 2014, 43 (3): 1- 4
CHEN Liang Analysis of current situation of domestic aluminum plate, strip and foil processing industry[J]. Nonferrous Metals Processing, 2014, 43 (3): 1- 4
[3]   颜云辉. 机器视觉检测与板带钢质量评价[M]. 北京: 科学出版社, 2016.
[4]   陈凯华. 基于机器视觉的板材表面缺陷检测与识别算法研究[D]. 南昌: 华东交通大学, 2012.
CHEN Kai-hua. Research based on machine vision of plate surface defects detection and recognition algorithm [D]. Nanchang: East China Jiaotong University, 2012.
[5]   秦钟伟, 陈捷, 洪荣晶, 等 摩擦片表面缺陷的视觉显著性检测算法[J]. 浙江大学学报: 工学版, 2019, 53 (10): 1883- 1891
QIN Zhong-wei, CHEN Jie, HONG Rong-jing, et al Visual salience detection algorithm for surface defects of friction sheets[J]. Journal of Zhejiang University: Engineering Science, 2019, 53 (10): 1883- 1891
[6]   ZHAO J, YANG Y M, LI G. The cold rolling strip surface defect on-line inspection system based on machine vision [C]// 2010 2nd Pacific-Asia Conference on Circuits, Communications and System. Beijing: IEEE, 2010: 402-405.
[7]   YANG Y X, LI Q, CHEN P, et al. Strip surface defect detection algorithm based on background difference [C]//2010 2nd Pacific-Asia Conference on Circuits, Communications and System. Beijing: IEEE, 2010: 23-26.
[8]   YANG Y X, DENG Y, LI Q, et al. Strip surface defect recognition algorithm based on PCA and improved BP neural network [C]// 2010 2nd Pacific-Asia Conference on Circuits, Communications and System. Beijing: IEEE, 2010: 19-22.
[9]   SUN X H, GU J N, TANG S X, et al Research progress of visual inspection technology of steel products: a review[J]. Applied Sciences, 2018, 8 (11): 2195
doi: 10.3390/app8112195
[10]   BENGIO Y, COURVILLE A, VINCENT P Representation learning: a review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35 (8): 1798- 1828
doi: 10.1109/TPAMI.2013.50
[11]   LECUN Y, BENGIO Y, HINTON G Deep learning[J]. Nature, 2015, 521 (7553): 436
doi: 10.1038/nature14539
[12]   LI J Y, SU Z F, GENG J H, et al. Real-time detection of steel strip surface defects based on improved YOLO detection network [C/OL]// 5th IFAC Workshop on Mining, Mineral and Metal Processing. Shanghai: IFAC, 2018 [2019-12-01]. https://doi.org/ 10.1016/j.ifacol.2018.09.412.
[13]   REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 779-788.
[14]   TAO X, ZHANG D P, MA W Z, et al Automatic metallic surface defect detection and recognition with convolutional neural networks[J]. Applied Sciences, 2018, 8 (9): 1575
doi: 10.3390/app8091575
[15]   YI L, LI G Y, JIANG M M An end-to-end steel strip surface defects recognition system based on convolutional neural networks[J]. Steel Research International, 2017, 88 (2): 176- 187
[16]   BARNICH O, DROOGENBROECK V M. VIBE: a powerful random technique to estimate the background in video sequences [C]// 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. Taipei: IEEE, 2009: 945-948.
[17]   BARNICH O, DROOGENBROECK V M ViBe: a universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing, 2011, 20 (6): 1709- 1724
doi: 10.1109/TIP.2010.2101613
[18]   陈亮, 陈晓竹, 范振涛 基于Vibe的鬼影抑制算法[J]. 中国计量学院学报, 2013, 24 (4): 425- 429
CHEN Liang, CHEN Xiao-zhu, FAN Zhen-tao Ghost suppression algorithm based on Vibe[J]. Journal of China University of Metrology, 2013, 24 (4): 425- 429
[19]   徐久强, 江萍萍, 朱宏博, 等 面向运动目标检测的ViBe算法改进[J]. 东北大学学报: 自然科学版, 2015, 36 (9): 1227- 1231
XU Jiu-qiang, JIANG Ping-ping, ZHU Hong-bo, et al An improved ViBe algorithm for moving object detection[J]. Journal of Northeastern University: Natural Science, 2015, 36 (9): 1227- 1231
[20]   BOUVRIE J. Notes on convolutional neural networks [EB/OL]. (2006-11-22) [2019-12-01]. http://web.mit.edu/jvb/www/papers/ cnn_tutorial.pdf.
[21]   张顺, 龚怡宏, 王进军 深度卷积神经网络的发展及其在计算机视觉领域的应用[J]. 计算机学报, 2019, 42 (3): 453- 482
ZHANG Shun, GONG Yi-hong, WANG Jin-jun The development of deep convolution neural network and its application on computer vision[J]. Chinese Journal of Computers, 2019, 42 (3): 453- 482
[22]   SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition [EB/OL]. (2015-04-10) [2019-12-01]. http://arxiv.org/abs/1409.1556.
[23]   安宗权, 王匀 一种非线性扩散与图像差分的金属表面缺陷检测方法[J]. 表面技术, 2018, 47 (6): 277- 283
AN Zong-quan, WANG Yun A metal surface defect detection method based on nonlinear diffusion and image difference[J]. Surface Technology, 2018, 47 (6): 277- 283
[1] Qiao-hong CHEN,YI CHEN,Wen-shu Li,Yu-bo JIA. Clothing image classification based on multi-scale SE-Xception[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(9): 1727-1735.
[2] Ping YANG,Dan WANG,Zi-jian KAGN,Tong LI,Li-hua FU,Yue-ren YU. Prediction model of paroxysmal atrial fibrillation based on pattern recognition and ensemble CNN-LSTM[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(5): 1039-1048.
[3] Hong-guang LI,Ying GUO,Ping SUI,Zi-sen QI. Frequency hopping modulation recognition of convolutional neural network based on time-frequency characteristics[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(10): 1945-1954.
[4] Zi-yu JIA,You-fang LIN,Hong-jun ZHANG,Jing WANG. Sleep stage classification model based ondeep convolutional neural network[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(10): 1899-1905.
[5] Yan LV,Meng ZHANG,Wu-hao JIANG,Yi-hua NI,Xiao-hong QIAN. Design of elderly fall detection system using CNN[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(6): 1130-1138.
[6] Gui-ran HE,Qi LI,Hua-jun FENG,Zhi-hai XU,Yue-ting CHEN. Dual-focal camera continuous digital zoom based onCNN and feature extraction[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(6): 1182-1189.
[7] Yue DONG,Hua-jun FENG,Zhi-hai XU,Yue-ting CHEN,Qi LI. Attention Res-Unet: an efficient shadow detection algorithm[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(2): 373-381.
[8] Jing-chang WANG,Ling CHEN,Shan-shan YU,Chen-shu JIANG,Yong WU. Multi-factor perceived short-term tourist number prediction model based on gated recurrent unit[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(12): 2357-2364.
[9] Hao LI,Wen-jie ZHAO,Bo HAN. Convolutional neural network acceleration algorithm based on filters pruning[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(10): 1994-2002.
[10] TANG You bao, BU Wei, WU Xiang qian. Natural scene text detection based on multi level MSER[J]. Journal of ZheJiang University (Engineering Science), 2016, 50(6): 1134-1140.