DiffusionLane: Diffusion Model for Lane Detection
In this paper, we present a novel diffusion-based model for lane detection,
called DiffusionLane, which treats the lane detection task as a denoising
diffusion process in the parameter space of the lane. Firstly, we add the
Gaussian noise to the parameters (the starting point and the angle) of ground
truth lanes to obtain noisy lane anchors, and the model learns to refine the
noisy lane anchors in a progressive way to obtain the target lanes. Secondly,
we propose a hybrid decoding strategy to address the poor feature
representation of the encoder, resulting from the noisy lane anchors.
Specifically, we design a hybrid diffusion decoder to combine global-level and
local-level decoders for high-quality lane anchors. Then, to improve the
feature representation of the encoder, we employ an auxiliary head in the
training stage to adopt the learnable lane anchors for enriching the
supervision on the encoder. Experimental results on four benchmarks, Carlane,
Tusimple, CULane, and LLAMAS, show that DiffusionLane possesses a strong
generalization ability and promising detection performance compared to the
previous state-of-the-art methods. For example, DiffusionLane with ResNet18
surpasses the existing methods by at least 1\% accuracy on the domain
adaptation dataset Carlane. Besides, DiffusionLane with MobileNetV4 gets
81.32\% F1 score on CULane, 96.89\% accuracy on Tusimple with ResNet34, and
97.59\% F1 score on LLAMAS with ResNet101. Code will be available at
https://github.com/zkyntu/UnLanedet.
6900a6a5646208eac0d1efb1
Kunyang Zhou
6900a6a5646208eac0d1efb2
Yeqin Shao
DiffusionLane: Diffusion Model for Lane Detection
In this paper, we present a novel diffusion-based model for lane detection,
called DiffusionLane, which treats the lane detection task as a denoising
diffusion process in the parameter space of the lane. Firstly, we add the
Gaussian noise to the parameters (the starting point and the angle) of ground
truth lanes to obtain noisy lane anchors, and the model learns to refine the
noisy lane anchors in a progressive way to obtain the target lanes. Secondly,
we propose a hybrid decoding strategy to address the poor feature
representation of the encoder, resulting from the noisy lane anchors.
Specifically, we design a hybrid diffusion decoder to combine global-level and
local-level decoders for high-quality lane anchors. Then, to improve the
feature representation of the encoder, we employ an auxiliary head in the
training stage to adopt the learnable lane anchors for enriching the
supervision on the encoder. Experimental results on four benchmarks, Carlane,
Tusimple, CULane, and LLAMAS, show that DiffusionLane possesses a strong
generalization ability and promising detection performance compared to the
previous state-of-the-art methods. For example, DiffusionLane with ResNet18
surpasses the existing methods by at least 1\% accuracy on the domain
adaptation dataset Carlane. Besides, DiffusionLane with MobileNetV4 gets
81.32\% F1 score on CULane, 96.89\% accuracy on Tusimple with ResNet34, and
97.59\% F1 score on LLAMAS with ResNet101. Code will be available at
https://github.com/zkyntu/UnLanedet.
DiffusionLane, a diffusion-based model, enhances lane detection by refining noisy lane anchors through a hybrid diffusion decoder and auxiliary head, achieving superior performance across multiple benchmarks.
diffusion-based model denoising diffusion process parameter space Gaussian noise noisy lane anchors hybrid decoding strategy global-level decoders local-level decoders high-quality lane anchors feature representation encoder auxiliary head domain adaptation Carlane Tusimple CULane LLAMAS ResNet18 MobileNetV4 ResNet34 ResNet101