A Fast and Robust Lane Detection via Online Re-Parameterization and Hybrid Attention

Abstract

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.

CoDC: Accurate Learning with Noisy Labels via Disagreement and Consistency

“…Inspired by the biological nervous system, deep neural networks (DNNs) have been successfully utilized in various tasks [1][2][3][4][5][6], particularly in computer vision [7][8][9][10][11][12]. This is made possible by large-scale datasets with accurate labels, although collecting them can be challenging and costly, especially in certain professional fields that require personnel with relevant professional knowledge to label samples.…”