Vehicle-Mounted Adaptive Traffic Sign Detector for Small-Sized Signs in Multiple Working Conditions

计算机科学 探测器 交通标志 人工智能 计算机视觉 实时计算 汽车工程 符号(数学) 运输工程 工程类 数学 电信 数学分析
作者
Junfan Wang,Yi Chen,Xiaoyue Ji,Zhekang Dong,Mingyu Gao,Chun Sing Lai
出处
期刊:IEEE Transactions on Intelligent Transportation Systems [Institute of Electrical and Electronics Engineers]
卷期号:25 (1): 710-724 被引量:17
标识
DOI:10.1109/tits.2023.3309644
摘要

Traffic sign detection is of great significance to the development of the Intelligent Transportation System (ITS) as a database for environmental awareness. The main challenges of existing traffic sign detection method are inaccurate small object detection, difficult mobile deployment, and complex working environment. Based on these, a vehicle-mounted adaptive traffic sign detector (VATSD) for small-sized signs in multiple working conditions is proposed in this paper. First, the Backbone of the detector is optimized. A feature tight fusion structure is designed to constitute a new feature extraction module, DCSP, which improves the feature extraction capability and the detection accuracy of small objects with negligible additional parameters. Second, an image enhancement network IENet with an adaptive joint filtering strategy is proposed. The IENet enables the dynamic selection of filters and thus adaptively optimizes low-quality images under multiple conditions to improve the accuracy of subsequent detection tasks. The proposed method has experimented on three traffic sign datasets and the detection accuracy increased by up to 7.6% compared to the original. The proposed detector demonstrates superiority over other state-of-the-art (SOTA) methods in terms of small object detection accuracy, detection speed, and environmental adaptability. Further, we deployed VATSD to Jetson Xavier NX and achieved a detection speed of 21.6 FPS, meeting real-time requirements.
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