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http://dx.doi.org/10.6109/jkiice.2021.25.1.36

Object Detection of AGV in Manufacturing Plants using Deep Learning  

Lee, Gil-Won (Wixcon Co. Ltd.)
Lee, Hwally (Hyundai Motor Company)
Cheong, Hee-Woon (Graduate School of Management of Technology, Hoseo University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.1, 2021 , pp. 36-43 More about this Journal
Abstract
In this research, the accuracy of YOLO v3 algorithm in object detection during AGV (Automated Guided Vehicle) operation was investigated. First of all, AGV with 2D LiDAR and stereo camera was prepared. AGV was driven along the route scanned with SLAM (Simultaneous Localization and Mapping) using 2D LiDAR while front objects were detected through stereo camera. In order to evaluate the accuracy of YOLO v3 algorithm, recall, AP (Average Precision), and mAP (mean Average Precision) of the algorithm were measured with a degree of machine learning. Experimental results show that mAP, precision, and recall are improved by 10%, 6.8%, and 16.4%, respectively, when YOLO v3 is fitted with 4000 training dataset and 500 testing dataset which were collected through online search and is trained additionally with 1200 dataset collected from the stereo camera on AGV.
Keywords
AGV; Deep learning; LiDAR; mAP; YOLO;
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1 S. Kaliappan, J. Lokesh, P. Mahaneesh, and M. Siva, "Mechanical Design and Analysis of AGV for Cost Reduction of Material Handling in Automobile Industries," International Research Journal of Automotive Technology, vol. 1, no. 1, pp. 1-7, Jan. 2018.
2 E. S. Maarif and T. Moyo, "Driving control module for low cost industrial automated guided vehicle," IOP Conference Series: Materials Science and Engineering, vol. 535, no. 1, pp. 1-13, Jun. 2019.
3 S. Simoncic and P. Podrzaj, "Vision‐based control of a line tracing mobile robot," Computer Applications in Engineering Education, vol. 22, no. 3, pp. 474-480, Aug. 2011.   DOI
4 J. Li, W. Chen, B. Li, and T. Wang, "Road recognition and tracking control of an vision guided AGV," Transactions of the Chinese Society for Agricultural Machinery, Feb. 2008.
5 G. Zhou, J. Yuan, I. Yen, and F. Bastani, "Robust real-time UAV based power line detection and tracking," in Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, pp. 744-748, 2016.
6 M. Lv, T. Gao, and N. Zhang, "Research of AGV scheduling and path planning of automatic transport system," International Journal of Control and Automation, vol. 9, no. 4, pp. 1-10, Apr. 2016.   DOI
7 C. Liu, J. Tan, H. Zhao, Y. Li, and X. Bai, "Path planning and intelligent scheduling of multi-AGV systems in workshop," in Proceedings of the 2017 36th Chinese Control Conference (CCC), Dalian, pp. 2735-2739, 2017.
8 S. Buck, R. Hanten, K. Bohlmann, and A. Zell, "Generic 3d obstacle detection for AGVs using time-of-flight cameras," in Proceedings of 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, pp. 4119-4124, 2016.
9 Y. Yang, M. Zhong, Y. Dessouky, and O. Postolache, "An integrated scheduling method for AGV routing in automated container terminals," Computers & Industrial Engineering, vol. 126, pp. 482-493, Dec. 2018.   DOI
10 M. V. Gomes, L. A. Bassora, O. Morandin, and K. C. T. Vivaldini, "PID control applied on a line-follower AGV using a RGB camera," in Proceedings of 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, pp. 194-198, 2016.
11 R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," in Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, pp. 580-587, 2014.
12 R. Girshick, "Fast R-CNN," in Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, pp. 1440-1448, 2015.
13 J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in Proceedings of 2016 IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, pp. 779-788, 2016.
14 S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," in Proceedings of Advances in Neural Information Processing Systems 28 (NIPS 2015), pp. 1-9, 2015.
15 K. He, G. Gkioxari, P. Dollar, and R. Girshick, "Mask R-CNN," in Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV), Venice, pp. 2980-2988, 2017.
16 J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders, "Selective search for object recognition," International journal of computer vision, vol. 104, no. 2, pp. 154-171, Sep. 2013.   DOI
17 J. Redmon and A. Farhadi, "YOLO9000: Better, Faster, Stronger," in Proceedings of 2017 IEEE conference on computer vision and pattern recognition (CVPR), Hawaii, pp. 7263-7271, 2017.