A Multi-object Detection Method Based on Connected Vehicles
Authors: 
Yunpeng Wang, Xixian Wang, Daxin Tian, Xuting Duan, He Liu, Yinsheng Gong, Zhengguo Sheng, Victor C.M. LeungAuthors Info & Claims
Yunpeng Wang, Xixian Wang, Daxin Tian, Xuting Duan, He Liu, Yinsheng Gong, Zhengguo Sheng, Victor C.M. LeungAuthors Info & ClaimsPages 89 - 96
Abstract
Nowadays, with internet of vehicle developing, more and more research institute begin to research intelligent transportation systems. Vehicle-road collaborative system is a prominent one of these systems. Its main function is to percept traffic situation. Using image recognition technology is one of the methods. The advantages of this method are low cost, high data-correcting rate, and small interference to traffic flow. Traditional image recognition algorithms always have problems with high-processing time and low accuracy, such as HOG and DPM. They are not suitable to monitor real-time traffic videos of numerous image frames. In this paper, the structure of convolutional neural network is improved, different from original YOLOv3 algorithms. Compared with original YOLOv3 algorithm, the algorithm in this paper can not only realize multi-object detection, but also consume time and recognize more accurate. Moreover, this paper constructs a special identification data set of common traffic participants (pedestrians, cars, buses, etc.) to monitor traffic flow, basing on INRIA and KITTI data sets. Moreover, this paper collects images of commonly-seen objects at traffic intersections to test the performance of convolutional neural network structure of this paper. At the end of this paper, the performance of the proposed algorithm is verified, based on the real-time monitoring video of traffic intersections. According to the results, for common traffic participants, the mean average precise is 13.2% higher than that of original YOLOv3 algorithm, and detection time is reduced by 7.8%.
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Published: 25 November 2019
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