随着信息技术在工业制造领域的深入应用, 工业制造大数据研究正成为实现智能制造、帮助政府指导制造企业转型升级的重要参考依据. 在传统的钢铁、铝材等金属制造行业, 更是存在生产方式粗放、生产工艺简单等问题. 因此, 迫切需要利用人工智能等新一代信息技术来改善生产流程，提高生产效率. 在使用铝材时, 必须检查铝材表面. 现有的铝材表面缺陷检测受限于传统人工肉眼检查, 十分费力, 或基于传统的机器视觉算法, 识别率不高, 通常不能及时准确地判断出表面瑕疵. 为解决这些问题，利用深度学习来进行铝材表面缺陷检测: 首先运用两大目标检测算法Faster R-CNN(Region-CNN(Convolutional Neural Networks))和YOLOv3对制作的铝材缺陷数据集进行检测; 然后基于YOLOv3算法进行改进, 提升铝材表面很小缺陷的检测效果. 在广东工业智造大数据创新大赛提供的“铝型材瑕疵识别”数据集上进行了实验验证, 实验结果显示, 改进算法的平均精度均值(mean Average Precision, mAP)比YOLOv3算法高3.4%, 比Faster R-CNN算法高1.8%.
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.
[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)