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2020 | OriginalPaper | Chapter

Monocular Vision-Based Real-Time Vehicle Detection at Container Terminals

Authors : Zijian Liu, Tianlei Zhang, Bei He, Yu Liu, Li Sun, Wenyang Tang

Published in: Proceedings of China SAE Congress 2018: Selected Papers

Publisher: Springer Singapore

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Abstract

We present a practical approach to vehicle detection at container terminals based on a single camera and prevailing convolutional neural network models in computer vision domain. Aiming at container terminal scenarios, we introduce a specialized data labelling strategy for network training, as well as an optimized setting of crucial hyperparameters, leading to a significant improvement on results. Our solution achieves 83% precision with 90% recall for semitrailer trunks within 30 m ahead of the vehicle-mounted monocular camera, at a speed of 32 frames per second (FPS) on a Nvidia Titan X for 416 × 416 image input, also providing more alternatives of fairly easy speed/accuracy trade-off. Compared to traditional lidar-based vehicle detection method for autonomous driving, our solution is more robust for particular container terminal scenarios while still maintaining a real-time performance by completely eliminating the region proposal stage.

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Literature
1.
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks[C]. In: International conference on neural information processing systems. Curran Associates Inc., pp 1097–1105
2.
Everingham M, Gool LV, Williams CKI et al (2010) The pascal visual object classes (VOC) challenge. Int J Comput Vis 88(2):303–338CrossRef
3.
Lin T, Maire M, Belongie SJ et al (2014) Microsoft COCO: common objects in context. In: European conference on computer vision, pp 740–755
4.
Felzenszwalb P, Mcallester D, Ramanan D (2008) A discriminatively trained, multiscale, deformable part model. In: Computer vision and pattern recognition, 2008. CVPR 2008. IEEE conference on. IEEE, pp 1–8
5.
Girshick RB, Donahue J, Darrell T et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Computer vision and pattern recognition, pp 580–587
6.
Girshick R (2015) Fast R-CNN[C]. In: IEEE international conference on computer vision. IEEE Computer Society, pp 1440–1448
7.
Ren S, Girshick R, Girshick R et al (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149CrossRef
8.
Liu W, Anguelov D, Erhan D et al (2016) SSD: single shot MultiBox detector[C]. In: European conference on computer vision. Springer, Cham, pp 21–37
9.
Redmon J, Divvala S, Girshick R et al (2016) You only look once: unified, real-time object detection[C]. In: IEEE conference on computer vision and pattern recognition. IEEE Computer Society, pp 779–788
10.
Geiger A (2012) Are we ready for autonomous driving? The KITTI vision benchmark suite[C]. In: IEEE conference on computer vision and pattern recognition. IEEE Computer Society, pp 3354–3361
11.
Uijlings JRR, Sande KEAVD, Gevers T et al (2013) Selective search for object recognition. Int J Comput Vis 104(2):154–171CrossRef
12.
Erhan D, Szegedy C, Toshev A et al (2014) Scalable object detection using deep neural networks[C]. In: IEEE conference on computer vision and pattern recognition. IEEE Computer Society, pp 2155–2162
13.
Howard AG, Zhu M, Chen B et al (2017) MobileNets: efficient convolutional neural networks for mobile vision applications
14.
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Comput Sci
15.
Chen X, Ma H, Wan J et al (2017) Multi-view 3D object detection network for autonomous driving. IEEE CVPR
Metadata
Title
Monocular Vision-Based Real-Time Vehicle Detection at Container Terminals
Authors
Zijian Liu
Tianlei Zhang
Bei He
Yu Liu
Li Sun
Wenyang Tang
Copyright Year
2020
Publisher
Springer Singapore
Book
Proceedings of China SAE Congress 2018: Selected Papers

Print ISBN: 978-981-13-9717-2

Electronic ISBN: 978-981-13-9718-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-13-9718-9_63

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