An Improved Real-Time Detection Transformer Model for the Intelligent Survey of Traffic Safety Facilities

Yan Wan, Hui Wang (), Lingxin Lu, Xin Lan, Feifei Xu and Shenglin Li
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Yan Wan: School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China
Hui Wang: Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, School of Civil Engineering, Chongqing University, Chongqing 400045, China
Lingxin Lu: Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, School of Civil Engineering, Chongqing University, Chongqing 400045, China
Xin Lan: Key Laboratory of New Technology for Construction of Cities in Mountain Area, Ministry of Education, School of Civil Engineering, Chongqing University, Chongqing 400045, China
Feifei Xu: School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China
Shenglin Li: College of Artificial Intelligence, Southwest University, Chongqing 400715, China

Sustainability, 2024, vol. 16, issue 23, 1-22

Abstract: The undertaking of traffic safety facility (TSF) surveys represents a significant labor-intensive endeavor, which is not sustainable in the long term. The subject of traffic safety facility recognition (TSFR) is beset with numerous challenges, including those associated with background misclassification, the diminutive dimensions of the targets, the spatial overlap of detection targets, and the failure to identify specific targets. In this study, transformer-based and YOLO (You Only Look Once) series target detection algorithms were employed to construct TSFR models to ensure both recognition accuracy and efficiency. The TSF image dataset, comprising six categories of TSFs in urban areas of three cities, was utilized for this research. The dimensions and intricacies of the Detection Transformer (DETR) family of models are considerably more substantial than those of the YOLO family. YOLO-World and Real-Time Detection Transformer (RT-DETR) models were optimal and comparable for the TSFR task, with the former exhibiting a higher detection efficiency and the latter a higher detection accuracy. The RT-DETR model exhibited a notable reduction in model complexity by 57% in comparison to the DINO (DETR with improved denoising anchor boxes for end-to-end object detection) model while also demonstrating a slight enhancement in recognition accuracy. The incorporation of the RepGFPN (Reparameterized Generalized Feature Pyramid Network) module has markedly enhanced the multi-target detection accuracy of RT-DETR, with a mean average precision (mAP) of 82.3%. The introduction of RepGFPN significantly enhanced the detection rate of traffic rods, traffic sign boards, and water surround barriers and somewhat ameliorated the problem of duplicate detection.

Keywords: traffic safety facility asset survey; traffic safety facility recognition; YOLO; real-time detection transformer; reparameterized generalized feature pyramid network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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