Work

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

Year: 2025

Type: article

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.

Source: Sustainability

DOI: 10.3390/su17093845

Authors: Yuxiang Qin, Xinzhou Qi, Ruochen Hao, Tuo Sun, Jun Song

Institutions: Tongji University, Hong Kong Baptist University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,896