The task of lane detection has garnered considerable attention in the field of autonomous driving due to its complexity. Lanes can present difficulties for detection, as they can be narrow, fragmented, and often obscured by heavy traffic. However, it has been observed that the lanes have a geometrical structure that resembles a straight line, leading to improved lane detection results when utilizing this characteristic. To address this challenge, we propose a hierarchical Deep Hough Transform (DHT) approach that combines all lane features in an image into the Hough parameter space. Additionally, we refine the point selection method and incorporate a Dynamic Convolution Module to effectively differentiate between lanes in the original image. Our network architecture comprises a backbone network, either a ResNet or Pyramid Vision Transformer, a Feature Pyramid Network as the neck to extract multi-scale features, and a hierarchical DHT-based feature aggregation head to accurately segment each lane. By utilizing the lane features in the Hough parameter space, the network learns dynamic convolution kernel parameters corresponding to each lane, allowing the Dynamic Convolution Module to effectively differentiate between lane features. Subsequently, the lane features are fed into the feature decoder, which predicts the final position of the lane. Our proposed network structure demonstrates improved performance in detecting heavily occluded or worn lane images, as evidenced by our extensive experimental results, which show that our method outperforms or is on par with state-of-the-art techniques.

翻译：车道线检测任务因其复杂性在自动驾驶领域引起了广泛关注。车道线可能因狭窄、断裂且常被密集交通遮挡而给检测带来困难。然而，研究发现车道线具有近似直线的几何结构，利用这一特性可有效提升检测效果。针对这一挑战，我们提出了一种分层深度霍夫变换（Deep Hough Transform, DHT）方法，将图像中所有车道线特征融合到霍夫参数空间中。同时，我们改进了点选择方法，并引入动态卷积模块（Dynamic Convolution Module）以有效区分原始图像中的不同车道线。我们的网络架构包含：骨干网络（ResNet或金字塔视觉变换器Pyramid Vision Transformer）、用于提取多尺度特征的颈部网络特征金字塔网络（Feature Pyramid Network），以及基于分层DHT的特征聚合头部网络，用以精确分割每条车道线。通过利用霍夫参数空间中的车道线特征，网络学习到对应每条车道线的动态卷积核参数，使动态卷积模块能够有效区分不同车道线特征。随后，车道线特征被输入特征解码器，预测车道线的最终位置。大量实验结果表明，我们提出的网络结构在检测严重遮挡或磨损的车道线图像时表现出更优性能，且方法性能优于或持平于当前最先进技术。