Xie Tao, Yin Mingfeng, Zhu Xinyu, Sun Jin, Meng Cheng, Bei Shaoyi
School of Automible and Traffic Engineering, Jiangsu University of Technology, Changzhou 213001, China.
Sensors (Basel). 2023 Oct 7;23(19):8285. doi: 10.3390/s23198285.
Lane detection is a vital component of intelligent driving systems, offering indispensable functionality to keep the vehicle within its designated lane, thereby reducing the risk of lane departure. However, the complexity of the traffic environment, coupled with the rapid movement of vehicles, creates many challenges for detection tasks. Current lane detection methods suffer from issues such as low feature extraction capability, poor real-time detection, and inadequate robustness. Addressing these issues, this paper proposes a lane detection algorithm that combines an online re-parameterization ResNet with a hybrid attention mechanism. Firstly, we replaced standard convolution with online re-parameterization convolution, simplifying the convolutional operations during the inference phase and subsequently reducing the detection time. In an effort to enhance the performance of the model, a hybrid attention module is incorporated to enhance the ability to focus on elongated targets. Finally, a row anchor lane detection method is introduced to analyze the existence and location of lane lines row by row in the image and output the predicted lane positions. The experimental outcomes illustrate that the model achieves F1 scores of 96.84% and 75.60% on the publicly available TuSimple and CULane lane datasets, respectively. Moreover, the inference speed reaches a notable 304 frames per second (FPS). The overall performance outperforms other detection models and fulfills the requirements of real-time responsiveness and robustness for lane detection tasks.
车道检测是智能驾驶系统的重要组成部分，为使车辆保持在指定车道内提供不可或缺的功能，从而降低车道偏离风险。然而，交通环境的复杂性以及车辆的快速移动给检测任务带来了诸多挑战。当前的车道检测方法存在特征提取能力低、实时检测效果差和鲁棒性不足等问题。针对这些问题，本文提出一种将在线重新参数化残差网络（ResNet）与混合注意力机制相结合的车道检测算法。首先，我们用在线重新参数化卷积取代标准卷积，简化推理阶段的卷积运算，进而减少检测时间。为提高模型性能，引入混合注意力模块以增强聚焦细长目标的能力。最后，引入逐行锚定车道检测方法，逐行分析图像中车道线的存在情况和位置，并输出预测的车道位置。实验结果表明，该模型在公开可用的TuSimple和CULane车道数据集上分别实现了96.84%和75.60%的F1分数。此外，推理速度达到了显著的每秒304帧（FPS）。整体性能优于其他检测模型，满足车道检测任务对实时响应性和鲁棒性的要求。
