With the application of information technology to industrial manufacturing, big data research in industrial manufacturing has become an important basis for realizing intelligent manufacturing and helping the government to guide the transformation and upgrade of manufacturing enterprises. In traditional metal manufacturing industries, like steel and aluminum, there are challenges, such as uncalibrated and rudimentary production techniques. Therefore, it is urgent to improve production processes and production efficiency by using newer generation information technology, such as artificial intelligence. The surface of aluminum, for example, must be inspected during the manufacturing process. Existing surface defect detection techniques for aluminum materials is limited to traditional manual naked eye inspection-which is very laborious-or traditional machine vision algorithms-which have a low rate of detection. Hence, surface defects cannot be accurately judged in a timely fashion. This paper studies the use of deep learning for surface defect detection of aluminum materials. Firstly, two major target detection algorithms, Faster R-CNN and YOLOv3, are used to detect the defects on a set of aluminum materials. Then, based on the YOLOv3 algorithm, the detection ability of small defects on aluminum surfaces was improved. We conducted experiments on the “Aluminum Profile Defect Recognition” dataset provided by the Guangdong Industrial Intelligent Manufacturing Big Data Innovation Competition. The experimental results showed that the mean average precision (mAP) of the improved algorithm is 3.4% higher than that of YOLOv 3 and 1.8% higher than that of Faster R-CNN.
ZHANG Xu
,
HUANG Dingjiang
. Defect detection on aluminum surfaces based on deep learning[J]. Journal of East China Normal University(Natural Science), 2020
, 2020(6)
: 105
-114
.
DOI: 10.3969/j.issn.1000-5641.201921021
[1] 相泽均. 冷轧钢板表面缺陷检测系统[J]. 周源, 译. 世界钢铁, 1994, 21(2): 66-73.
[2] 陈妍. 冷轧带钢材表面缺陷智能检测技术的发展 [J]. 鞍钢技术, 1998(9): 25-30
[3] 刘娜, 强锡富, 高若云. 图像处理在非接触测量中的应用 [J]. 电测与仪表, 1998(11): 44-45
[4] 罗志勇, 刘栋玉, 江涛, 等. 新型冷轧带钢表面缺陷在线检测系统 [J]. 华中理工大学学报, 1996(1): 75-78
[5] LIU B, WU S, ZOU S F. Automatic detection technology of surface defects on plastic products based on machine vision [C]// 2010 International Conference on Mechanic Automation and Control Engineering (MACE). IEEE, 2010: 2213-2216. DOI: 10.1109/MACE.2010.5536470.
[6] DENG S E, CAI W W, XU Q Y, et al. Defect detection of bearing surfaces based on machine vision technique [C]// 2010 International Conference on Computer Application and System Modeling. IEEE, 2010: V4-548-V4-554. DOI: 10.1109/ICCASM.2010.5620311.
[7] 贾方庆. 基于机器视觉的带钢表面缺陷检测系统研究 [D]. 重庆: 重庆大学, 2007.
[8] 谭绍华. 基于机器视觉的带钢表面缺陷检测系统研究 [D]. 武汉: 华中科技大学, 2012.
[9] 刘孟轲, 吴洋, 王逊. 基于卷积神经网络的轨道表面缺陷检测技术实现 [J]. 现代计算机(专业版), 2017(29): 67-71,79
[10] 刘雄祥. 基于卷积神经网络的铁轨表面缺陷识别研究 [D]. 四川 绵阳: 西南科技大学, 2018.
[11] GIRSHICK R. Fast R-CNN [C]// 2015 IEEE International Conference on Computer Vision(ICCV). IEEE, 2015: 1440-1448. DOI: 10.1109/ICCV.2015.169.
[12] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149
[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(CVPR). IEEE, 2016: 779-788. DOI: 10.1109/CVPR.2016.91.
[14] LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector [C]// Computer Vision - ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9905. Cham: Springer, 2016: 21-37.
[15] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection [C]// 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). IEEE, 2005: 886-893. DOI: 10.1109/CVPR.2005.177.
[16] LOWE D G. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision(IJCV), 2004, 60(2): 91-110
[17] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition [J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324
[18] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks [J]. Communications of the ACM, 2017, 60(6): 84-90
[19] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition [EB/OL]. (2015-04-10)[2019-06-15]. https://arxiv.org/abs/1409.1556.
[20] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). IEEE, 2016: 770-778. DOI: 10.1109/CVPR.2016.90.
[21] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C]// 2014 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2014: 580-587. DOI: 10.1109/CVPR.2014.81.
[22] HUANG L C, YANG Y, DENG Y F, et al. DenseBox: Unifying landmark localization with end to end object detection [EB/OL]. (2015-09-19)[2019-06-15]. http://export.arxiv.org/pdf/1509.04874v3.
[23] ZHU C C, HE Y H, SAVVIDES M. Feature Selective anchor-free module for single-shot object detection [C]//2019 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). IEEE, 2019: 840-849. DOI: 10.1109/CVPR.2019.00093.
[24] REDMON J, FARHADI A. YOLOv3: An incremental improvement [EB/OL]. (2018-04-08)[2019-06-15]. https://arxiv.org/abs/1804.02767.
中国综合性科技类核心期刊(北大核心)