• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A method based on Multi-Convolution layers Joint and Generative Adversarial Networks for Vehicle Detection

Vol. 13, No. 4, April 29, 2019
10.3837/tiis.2019.04.003, Download Paper (Free):

Abstract

In order to achieve rapid and accurate detection of vehicle objects in complex traffic conditions, we propose a novel vehicle detection method. Firstly, more contextual and small-object vehicle information can be obtained by our Joint Feature Network (JFN). Secondly, our Evolved Region Proposal Network (EPRN) generates initial anchor boxes by adding an improved version of the region proposal network in this network, and at the same time filters out a large number of false vehicle boxes by soft-Non Maximum Suppression (NMS). Then, our Mask Network (MaskN) generates an example that includes the vehicle occlusion, the generator and discriminator can learn from each other in order to further improve the vehicle object detection capability. Finally, these candidate vehicle detection boxes are optimized to obtain the final vehicle detection boxes by the Fine-Tuning Network(FTN). Through the evaluation experiment on the DETRAC benchmark dataset, we find that in terms of mAP, our method exceeds Faster-RCNN by 11.15%, YOLO by 11.88%, and EB by 1.64%. Besides, our algorithm also has achieved top2 comaring with MS-CNN, YOLO-v3, RefineNet, RetinaNet, Faster-rcnn, DSSD and YOLO-v2 of vehicle category in KITTI dataset.


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Cite this article

[IEEE Style]
G. Han, J. Su and C. Zhang, "A method based on Multi-Convolution layers Joint and Generative Adversarial Networks for Vehicle Detection," KSII Transactions on Internet and Information Systems, vol. 13, no. 4, pp. 1795-1811, 2019. DOI: 10.3837/tiis.2019.04.003.

[ACM Style]
Guang Han, Jinpeng Su, and Chengwei Zhang. 2019. A method based on Multi-Convolution layers Joint and Generative Adversarial Networks for Vehicle Detection. KSII Transactions on Internet and Information Systems, 13, 4, (2019), 1795-1811. DOI: 10.3837/tiis.2019.04.003.