Affiliation:
1. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2. College of Internet of Things, Nanjing University of Posts and Telecommunications, Jiangsu Key Laboratory of Broadband Wireless Communication and Internet of Things, Nanjing 210003, China
Abstract
Multitarget detection in complex traffic scenarios usually has many problems: missed detection of targets, difficult detection of small targets, etc. In order to solve these problems, this paper proposes a two-step detection model of depth-perception traffic scenarios to improve detection accuracy, mainly for three categories of frequently occurring targets: vehicles, person, and traffic signs. The first step is to use the optimized convolutional neural network (CNN) model to identify the existence of small targets, positioning them with candidate box. The second step is to obtain classification, location, and pixel-level segmentation of multitarget by using mask R-CNN based on the results of the first step. Without significantly reducing the detection speed, the two-step detection model can effectively improve the detection accuracy of complex traffic scenes containing multiple targets, especially small targets. In the actual testing dataset, compared with mask R-CNN, the mean average detection accuracy of multiple targets increased by 4.01% and the average precision of small targets has increased by 5.8%.
Funder
National Natural Science Foundation of China
Subject
General Engineering,General Mathematics
Cited by
2 articles.
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1. A review of 6G autonomous intelligent transportation systems: Mechanisms, applications and challenges;Journal of Systems Architecture;2023-09
2. Traffic Sign Detection and Recognition;International Journal of Advanced Research in Science, Communication and Technology;2022-05-22