ASO-ERFNet: An Adaptive Structural Optimization and Enhanced Receptive Field Network for Lane Detection in Vehicle Tracking

Lane detection, as one of the key real-time tasks in vehicle tracking, often faces numerous challenges due to the complex shapes and inherent symmetry of lane lines. Aiming at the requirements of high accuracy and real-time performance of the lane detection, this paper proposes an adaptive structural optimization and enhanced receptive field network for lane detection (ASO-ERFNet). Specifically, we propose a Multi-receptive Field Fusion Layer (MFFL) that optimizes the structure of the pooling operation to fully utilize its hierarchical characteristics, leverage the symmetry of lane layouts, and precisely preserve the spatial geometric properties of lane lines. Then, we design a Dynamic Cross Attention Mechanism (DCAM) that can adaptively adjust convolutional kernel weights, thereby further enhancing detection precision. In addition, compared with the existing single-feature extraction networks, we propose a Dual-Perception Network (DPNet) that fuses features from different scale levels through a U-shaped structure. The experimental findings show that compared with state-of-the-art methods, our model obtains superior performance, achieving the best results in six scenes, such as normal and crowded scenes. Specifically, our method achieved 76.61% and 79.90% performance in crowded and shadow scenes, respectively, while reducing the false positive rate by 22.90% in cross scenes. Moreover, with an average processing time of only 5.5 ms per frame, our method achieves an F1 score of 96.65% on the TuSimple dataset and 76.23% on the CULane dataset. These results indicate that our model effectively balances detection accuracy and speed by leveraging lane symmetry, providing an efficient and reliable solution for lane detection in vehicle tracking.