Efficient Roadside Vehicle Line-Pressing Identification in Intelligent Transportation Systems with Mask-Guided Attention

Yuxiang Qin, Xinzhou Qi, Ruochen Hao (), Tuo Sun and Jun Song
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Yuxiang Qin: School of Computer Science and Technology, Tongji University, Shanghai 201804, China
Xinzhou Qi: Zhaobian (Shanghai) Technology Co., Ltd., Shanghai 201804, China
Ruochen Hao: Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
Tuo Sun: Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
Jun Song: Department of Geography, Hong Kong Baptist University, Hong Kong 999077, China

Sustainability, 2025, vol. 17, issue 9, 1-21

Abstract: Vehicle line-pressing identification from a roadside perspective is a challenging task in intelligent transportation systems. Factors such as vehicle pose and environmental lighting significantly affect identification performance, and the high cost of data collection further exacerbates the problem. Existing methods struggle to achieve robust results across different scenarios. To improve the robustness of roadside vehicle line-pressing identification, we propose an efficient method. First, we construct the first large-scale vehicle line-pressing dataset based on roadside cameras (VLPI-RC). Second, we design an end-to-end convolutional neural network that integrates vehicle and lane line mask features, incorporating a mask-guided attention module to focus on key regions relevant to line-pressing events. Finally, we introduce a binary balanced contrastive loss (BBCL) to improve the model’s ability to generate more discriminative features, addressing the class imbalance issue in binary classification tasks. Experimental results demonstrate that our method achieves 98.65% accuracy and 96.34% F1 on the VLPI-RC dataset. Moreover, when integrated into the YOLOv5 object detection framework, it attains an identification speed of 108.29 FPS. These results highlight the effectiveness of our approach in accurately and efficiently detecting vehicle line-pressing behaviors.

Keywords: intelligent transportation systems; vehicle line-pressing identification; convolution neural network; mask-guided attention; imbalanced data classification (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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