Please use this identifier to cite or link to this item:
Title: Vehicle-Mounted Adaptive Traffic Sign Detector for Small-Sized Signs in Multiple Working Conditions
Authors: Wang, J
Chen, Y
Ji, X
Dong, Z
Gao, M
Lai, CS
Keywords: adaptive joint filtering;image enhancement;small objects;traffic sign detection
Issue Date: 11-Sep-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Wang, J. et al. (2023) 'Vehicle-Mounted Adaptive Traffic Sign Detector for Small-Sized Signs in Multiple Working Conditions', IEEE Transactions on Intelligent Transportation Systems, 25 (1), pp. 710 - 724. doi: 10.1109/TITS.2023.3309644.
Abstract: 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.
ISSN: 1524-9050
Other Identifiers: ORCiD: Junfan Wang
ORCiD: Xiaoyue Ji
ORCiD: Zhekang Dong
ORCiD: Mingyu Gao
ORCiD: Chun Sing Lai
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdfCopyright © 2023 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to For more information, see MBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.