  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.
