SGNet

Project for the IJCAI 2021 paper "Structure Guided Lane Detection"

Abstract

Recently, lane detection has made great progress with the rapid development of deep neural networks and autonomous driving. However, there exist three mainly problems including characterizing lanes, modeling the structural relationship between scenes and lanes, and supporting more attributes (e.g., instance and type) of lanes. In this paper, we propose a novel structure guided framework to solve these problems simultaneously. In the framework, we first introduce a new lane representation to characterize each instance. Then a topdown vanishing point guided anchoring mechanism is proposed to produce intensive anchors, which efficiently capture various lanes. Next, multi-level structural constraints are used to improve the perception of lanes. In the process, pixel-level perception with binary segmentation is introduced to promote features around anchors and restore lane details from bottom up, a lane-level relation is put forward to model structures (i.e., parallel) around lanes, and an image-level attention is used to adaptively attend different regions of the image from the perspective of scenes. With the help of structural guidance, anchors are effectively classified and regressed to obtain precise locations and shapes. Extensive experiments on public benchmark datasets show that the proposed approach outperforms stateof- the-art methods with 117 FPS on a single GPU.

Method

Framework of our approach. We first extract the common features by the extractor, which provides features for vanishing point guided anchoring and pixel-level perception. The anchoring produces intensive anchors and perception utilizes binary segmentation to promote features around lanes. Promoted features are used to classify and regress anchors with the aid of lane-level relation and image-level attention. The dashed arrow indicates the supervision, and the supervision of vanishing point and lane segmentation is omitted in the figure.

Quantitative Evaluation

Qualitative Evaluation

Usage

Dataset Convertion

For CULane, run

python datasets/2_generate_vp_label_dist_culane.py

For Tusimple, run

.datasets/gen_tusimple.sh

NMS Installation

cd lib/nms; python setup.py install

Training

python main.py train --exp_name workdir --cfg cfgs/resnet34.py

Testing

python main.py test --exp_name workdir --cfg cfgs/resnet34.py

Evaluation

cd evaluateion/lane_evaluation
make
./run.sh 
./run_all.sh

Visualization

python main.py test -exp_name workdir --view all

Thanks for the reference provided by the smart code.

Citation

@inproceedings{su2021structure,
  title={Structure Guided Lane Detection},
  author={Su, Jinming and Chen, Chao and Zhang, Ke and Luo, Junfeng and Wei, Xiaoming and Wei, Xiaolin},
  booktitle={International Joint Conference on Artificial Intelligence (IJCAI)},
  year={2021}
}

Pyten / SGNet