TL;DR
Extensive experiments demonstrate the superiority of CLRDFNet, which achieves a 96.90% Accuracy and 97.96% F1 on TuSimple with ResNet34 backbone, alongside 56.98% mF1 and 82.76% CULane with DLA34 backbone.
Lane detection is a critical task for autonomous driving and advanced driver assistance systems. Existing methods often suffer from insufficient fusion of global structure and local details, as well as underutilization of geometric priors such as vanishing point convergence. This work presents a structure-aware feature...
SRLane is a two-stage anchor-based method for lane detection, composed of proposal generation stage (Sketch) and Refinement stage, which improves SRLane on both stages.
The increasing volume of vehicular traffic and the progressive deterioration of road infrastructure demand intelligent and automated monitoring systems to enhance road safety and support timely maintenance. This paper presents AutoMed, a unified deep learning framework designed for real-time lane boundary detection and...
The proposed WTM-YOLOv11 enhances the detection performance of small and occluded targets in drone aerial imagery, providing a robust and feasible solution for intelligent public security surveillance systems.
Comparisons with mainstream state-of-the-art (SOTA) algorithms confirm that the proposed SDM-RTDETR method strikes a competitive trade-off between detection accuracy and model inference efficiency, demonstrating substantial engineering applicability in complex autonomous driving environments.
The results presented in this study show promising potential to integrate into a driver-assistance system, although the data used here is limited to a proof-of-concept validation on the KITTI dataset.
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