Research on Automatic Recognition Algorithm of OBD Port Occupancy State Based on Deep Learning

doi:10.13190/j.jbupt.2019-180

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2019, Vol. 42 ›› Issue (6): 49-57.doi: 10.13190/j.jbupt.2019-180

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Research on Automatic Recognition Algorithm of OBD Port Occupancy State Based on Deep Learning

SU Dong, YU Ning-mei

School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China

Received:2019-09-03 Online:2019-12-28 Published:2019-11-15

Abstract

Abstract: Because that optical branching device (OBD) port occupancy state can not be automatically acquired, an improved YOLOv3 algorithm was proposed. Firstly, by adding the fourth upsampling feature map, the detection sensitivity of dense small objects at high resolution is increased. Secondly, for characteristics of the fixed height-width ratio of port, the k-means clustering algorithm is used to re-determine the number of target candidate boxes and the height-width ratio. Thirdly, the soft non-maximum suppression algorithm is proposed to alleviate the missed detection and false detection caused by the proximity and occlusion of the port. Finally, four difficult production scenario in the port occupancy state is detected to verify the performance of the improved YOLOv3 algorithm. Experiments show that the accuracy of the improved YOLOv3 is 90.12%, 5.17% higher than the original YOLOv3. In conclusion, the improved algorithm has higher detection accuracy for port-like objects.

Key words: optical branching device, YOLOv3, clustering algorithm, soft non-maximum suppression, feature map

CLC Number:

TN913.7

SU Dong, YU Ning-mei. Research on Automatic Recognition Algorithm of OBD Port Occupancy State Based on Deep Learning[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(6): 49-57.

References

[1] Llorca D F, Arroyo R, Sotelo M A. Vehicle logo recognition in traffic images using HOG features and SVM[C]//16^th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013). Hague:IEEE Press, 2013:2229-2234.
[2] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2):91-110.
[3] 闫敬文, 陈宏达, 刘蕾. 高光谱图像分类的研究进展[J]. 光学精密工程, 2019, 27(3):680-693. Yan Jingwen, Chen Hongda, Liu Lei. Overview of hyperspectral image classification[J]. Optics and Precision Engineering, 2019, 27(3):680-693.
[4] Zhang X, Yang W, Tang X, et al. A fast learning method for accurate and robust lane detection using two-stage feature extraction with YOLO v3[J]. Sensors, 2018, 18(12), 4308-4320.
[5] Zhang J P, Zhang J M, Yu S. Hot anchors:a heuristic anchors sampling method in rcnn-based object detection[J]. Sensors, 2018, 18(10):3415-3431.
[6] 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). Las Vegas:IEEE Press, 2016:779-788.
[7] Liu W, Anguelov D, Erhan D, et al. Ssd:single shot multibox detector[C]//European Conference on Computer Vision. Amsterdam:Springer press, 2016:21-37.
[8] 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. Ohio:IEEE Press, 2014:580-587.
[9] Girshick R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision (ICCV).Chile:IEEE Press, 2015:1440-1448.
[10] Ren S, He K, Girshick R, et al. Faster r-cnn:towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems. Montreal:MIT press, 2015:91-99.
[11] Joseph Redmon, Ali Farhadi. YOLO9000:better, faster, stronger[C]//2017 IEEE Computer Vision and Pattern Recognition (CVPR).Hawaii:IEEE Press, 2017:6517-6525.
[12] Joseph Redmon, Ali Farhadi. YOLOv3:an incremental improvement[J]. arXiv:Computer Vision and Pattern Recognition, 2018:1804,02767.
[13] Bodla N, Singh B, Chellappa R, et al. Soft-NMS:improving object detection with one line of code[C]//2017 IEEE International Conference on Computer Vision (ICCV). Venice:IEEE Press, 2017:5561-5569.
[14] Ding J, Chen B, Liu H, et al. Convolutional neural network with data augmentation for SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3):364-368.
[15] Pan S J, Yang Q. A survey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10):1345-1359.
[16] Lin T Y, Maire M, Belongie S, et al. Microsoft COCO:common objects in context[M]//Computer Vision-ECCV 2014. Zurich:Springer International Publishing, 2014:740-755.
[17] Kingma D P, Ba J. Adam:a method for stochastic optimization[C]//International Conference for Learning Representations. California:arXiv preprint, 2014:1412, 6980.

Research on Automatic Recognition Algorithm of OBD Port Occupancy State Based on Deep Learning

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