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영문 논문지

홈 홈 > 연구문헌 > 영문 논문지 > TIIS (한국인터넷정보학회)

TIIS (한국인터넷정보학회)

Current Result Document : 3 / 27 이전건 이전건   다음건 다음건

한글제목(Korean Title) A method based on Multi-Convolution layers Joint and Generative Adversarial Networks for Vehicle Detection
영문제목(English Title) A method based on Multi-Convolution layers Joint and Generative Adversarial Networks for Vehicle Detection
저자(Author) Guang Han   Jinpeng Su   Chengwei Zhang  
원문수록처(Citation) VOL 13 NO. 04 PP. 1795 ~ 1811 (2019. 04)
한글내용
(Korean Abstract)
영문내용
(English 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.
키워드(Keyword) Vehicle detection   non-maximum suppression   generative adversarial networks   joint feature map   mask occlusion  
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