Fire and smoke detection in electric vehicle environments is essential for safety monitoring, yet existing methods often perform poorly under low-light conditions because of weak feature representation, flame overexposure, and limited nighttime data. In addition, many detection models cannot effectively balance accuracy and real-time efficiency, which restricts their deployment in practical applications. To address these issues, this study proposes a lightweight dual-branch detection framework for low-light fire and smoke recognition in electric vehicle environments. The proposed method integrates reflectance enhancement, adaptive feature weighting, and multi-scale feature learning within a... More >

This paper proposes an enhanced U-Net-based segmentation framework for road crack detection that effectively addresses issues such as incomplete segmentation, detail loss, environmental complexity, and crack-pixel imbalance. The model integrates multiple functional modules to improve segmentation performance across varying crack types and scales. Specifically, an atrous residual convolution (ARC) module is embedded in the encoder to expand the receptive field and capture large-scale features. A multiple attention fusion module (MAFM), combined with an efficient channel attention mechanism, is introduced at the bridge stage to emphasize crack-relevant features. In the decoder, a defect correc... More >

Bridge surface defect detection plays a critical role in ensuring traffic safety and facilitating infrastructure maintenance. A lightweight object detection network based on YOLOv10, termed LBSD-YOLO, is developed to achieve high detection accuracy while maintaining high efficiency for deployment on resource-constrained devices. The proposed framework consists of three main components: a feature extraction backbone, a feature fusion neck, and a detection head. In the backbone, the C2f\_FEMA (C2f with Feature Enhancement and Multi-branch Attention) module and the LAEDS (Lightweight Adaptive Encoder–Decoder for Sampling) spatial attention module are incorporated to enhance multi-scale featur... More >

Fatigue driving is widely recognized as one of the major factors contributing to traffic accidents, posing not only a serious threat to road safety but also potential risks to drivers’ health and public security. With the rapid development of modern transportation, how to efficiently and accurately detect and warn against driver fatigue has become a critical issue in the field of intelligent transportation. To effectively address this issue, this paper proposes a novel fatigue driving detection method based on a Multi-Head Transformer with Adaptive Weighted Loss. In the proposed framework, the YOLOv8 model is first employed to efficiently and accurately locate key facial regions of the dri... More >