autowarefoundation · StepTurtle · May 29, 2024 · May 29, 2024 · May 29, 2024 · May 29, 2024
diff --git a/docs/how-to-guides/index.md b/docs/how-to-guides/index.md
@@ -19,6 +19,7 @@
 - [Applying Clang-Tidy to ROS packages](others/applying-clang-tidy-to-ros-packages.md)
 - [Defining temporal performance metrics on components](others/defining-temporal-performance-metrics.md)
 - [An example procedure for adding and evaluating a new node](others/an-example-procedure-for-adding-and-evaluating-a-new-node.md)
+- [Lane Detection Methods](others/lane-detection-methods.md)
 
 TODO: Write the following contents.
 

diff --git a/docs/how-to-guides/others/lane-detection-methods.md b/docs/how-to-guides/others/lane-detection-methods.md
@@ -0,0 +1,183 @@
+# Lane Detection Methods
+
+## Overview
+
+This document describes some of the most common lane detection methods used in the autonomous driving industry.
+Lane detection is a crucial task in autonomous driving, as it is used to determine the boundaries of the road and the
+vehicle's position within the lane.
+
+## Methods
+
+This document covers the methods under two categories: lane detection methods and multitask detection methods.
+
+!!! note
+
+    The results have been obtained using pre-trained models. Training the model with your own data will yield more
+    successful results.
+
+### Lane Detection Methods
+
+#### CLRerNet
+
+This work introduce LaneIoU, which improves confidence score accuracy by considering local lane angles, and CLRerNet,
+a novel detector leveraging LaneIoU.
+
+- **Paper**: [CLRerNet: Improving Confidence of Lane Detection with LaneIoU](https://arxiv.org/abs/2305.08366)
+- **Code**: [GitHub](https://github.com/hirotomusiker/CLRerNet)
+
+| Method   | Backbone | Dataset | Confidence | Campus Video                                             | Road Video                                               |
+| -------- | -------- | ------- | ---------- | -------------------------------------------------------- | -------------------------------------------------------- |
+| CLRerNet | dla34    | culane  | 0.4        | ![type:video](https://www.youtube.com/embed/bfuHuoembGg) | ![type:video](https://www.youtube.com/embed/9r_IEg_IkJ8) |
+| CLRerNet | dla34    | culane  | 0.1        | ![type:video](https://www.youtube.com/embed/XVonhGmvt8Q) | ![type:video](https://www.youtube.com/embed/5P6-yqCPAns) |
+| CLRerNet | dla34    | culane  | 0.01       | ![type:video](https://www.youtube.com/embed/Sp599_HyegU) | ![type:video](https://www.youtube.com/embed/2tz9gXNIjqs) |
+
+#### CLRNet
+
+This work introduce Cross Layer Refinement Network (CLRNet) to fully utilize high-level semantic and low-level detailed
+features in lane detection.
+CLRNet detects lanes with high-level features and refines them with low-level details.
+Additionally, ROIGather technique and Line IoU loss significantly enhance localization accuracy,
+outperforming state-of-the-art methods.
+
+- **Paper**: [CLRNet: Cross Layer Refinement Network for Lane Detection](https://arxiv.org/abs/2203.10350)
+- **Code**: [GitHub](https://github.com/Turoad/CLRNet)
+
+| Method | Backbone  | Dataset  | Confidence | Campus Video                                             | Road Video                                               |
+| ------ | --------- | -------- | ---------- | -------------------------------------------------------- | -------------------------------------------------------- |
+| CLRNet | dla34     | culane   | 0.2        | ![type:video](https://www.youtube.com/embed/n2HpKlOKGvc) | ![type:video](https://www.youtube.com/embed/K6-AHSraopc) |
+| CLRNet | dla34     | culane   | 0.1        | ![type:video](https://www.youtube.com/embed/BWdCEFy6k3w) | ![type:video](https://www.youtube.com/embed/dHrzsIotVWA) |
+| CLRNet | dla34     | culane   | 0.01       | ![type:video](https://www.youtube.com/embed/5iNo2VMD9os) | ![type:video](https://www.youtube.com/embed/nl4Lthr1mT8) |
+| CLRNet | dla34     | llamas   | 0.4        | ![type:video](https://www.youtube.com/embed/IxzJ7TfaSrk) | ![type:video](https://www.youtube.com/embed/IxDdOI5efl0) |
+| CLRNet | dla34     | llamas   | 0.2        | ![type:video](https://www.youtube.com/embed/vaR8tAgB1Ew) | ![type:video](https://www.youtube.com/embed/vc8-kslVi34) |
+| CLRNet | dla34     | llamas   | 0.1        | ![type:video](https://www.youtube.com/embed/LkDeZQOItqw) | ![type:video](https://www.youtube.com/embed/r-O92vxSXuw) |
+| CLRNet | resnet18  | llamas   | 0.4        | ![type:video](https://www.youtube.com/embed/nqSupblM89o) | ![type:video](https://www.youtube.com/embed/py1S5fDIC5E) |
+| CLRNet | resnet18  | llamas   | 0.2        | ![type:video](https://www.youtube.com/embed/rrNoXck6YLc) | ![type:video](https://www.youtube.com/embed/KHaS9GXueJg) |
+| CLRNet | resnet18  | llamas   | 0.1        | ![type:video](https://www.youtube.com/embed/J-gU1xbba28) | ![type:video](https://www.youtube.com/embed/5U3O0iaUWF4) |
+| CLRNet | resnet18  | tusimple | 0.2        | ![type:video](https://www.youtube.com/embed/HfZYdADQsPM) | ![type:video](https://www.youtube.com/embed/syse16SpafY) |
+| CLRNet | resnet18  | tusimple | 0.1        | ![type:video](https://www.youtube.com/embed/o3w3wL_f-GY) | ![type:video](https://www.youtube.com/embed/O2HwNfTJvSQ) |
+| CLRNet | resnet34  | culane   | 0.1        | ![type:video](https://www.youtube.com/embed/6IgkfJsCjWA) | ![type:video](https://www.youtube.com/embed/LgU3mQniP8c) |
+| CLRNet | resnet34  | culane   | 0.05       | ![type:video](https://www.youtube.com/embed/eLLcPrEpy84) | ![type:video](https://www.youtube.com/embed/fPoP3uFpzRw) |
+| CLRNet | resnet101 | culane   | 0.2        | ![type:video](https://www.youtube.com/embed/FODj_M-RRC4) | ![type:video](https://www.youtube.com/embed/5fG8ApvTFD4) |
+| CLRNet | resnet101 | culane   | 0.1        | ![type:video](https://www.youtube.com/embed/i0Bu2-Ef8T8) | ![type:video](https://www.youtube.com/embed/DWy4HeHyZYQ) |
+
+#### FENet
+
+This research introduces Focusing Sampling, Partial Field of View Evaluation, Enhanced FPN architecture,
+and Directional IoU Loss, addressing challenges in precise lane detection for autonomous driving.
+Experiments show that Focusing Sampling, which emphasizes distant details crucial for safety,
+significantly improves both benchmark and practical curved/distant lane recognition accuracy over uniform approaches.
+
+- **Paper**: [FENet: Focusing Enhanced Network for Lane Detection](https://arxiv.org/abs/2312.17163)
+- **Code**: [GitHub](https://github.com/HanyangZhong/FENet)
+
+| Method   | Backbone | Dataset | Confidence | Campus Video                                             | Road Video                                               |
+| -------- | -------- | ------- | ---------- | -------------------------------------------------------- | -------------------------------------------------------- |
+| FENet v1 | dla34    | culane  | 0.2        | ![type:video](https://www.youtube.com/embed/eGHgxf-8mcg) | ![type:video](https://www.youtube.com/embed/YMKCWLWq2Ww) |
+| FENet v1 | dla34    | culane  | 0.1        | ![type:video](https://www.youtube.com/embed/em3eaZ6RKZM) | ![type:video](https://www.youtube.com/embed/bCjEUtoIYac) |
+| FENet v1 | dla34    | culane  | 0.05       | ![type:video](https://www.youtube.com/embed/_3gwLW54aHw) | ![type:video](https://www.youtube.com/embed/24hjuNlZBIQ) |
+| FENet v2 | dla34    | culane  | 0.2        | ![type:video](https://www.youtube.com/embed/Z4WPJ9Cop2w) | ![type:video](https://www.youtube.com/embed/d3bovsjF2tE) |
+| FENet v2 | dla34    | culane  | 0.1        | ![type:video](https://www.youtube.com/embed/vbE1wNIc1Js) | ![type:video](https://www.youtube.com/embed/ezWGPTSbBAw) |
+| FENet v2 | dla34    | culane  | 0.05       | ![type:video](https://www.youtube.com/embed/sJvyR6jrlpY) | ![type:video](https://www.youtube.com/embed/XKuJ-YoVusY) |
+| FENet v2 | dla34    | llamas  | 0.4        | ![type:video](https://www.youtube.com/embed/_GrUe6phC7U) | ![type:video](https://www.youtube.com/embed/_YLDS-gTg2w) |
+| FENet v2 | dla34    | llamas  | 0.2        | ![type:video](https://www.youtube.com/embed/G59KpXE-2OI) | ![type:video](https://www.youtube.com/embed/3MaNauiPAxQ) |
+| FENet v2 | dla34    | llamas  | 0.1        | ![type:video](https://www.youtube.com/embed/cre7XhUF7IM) | ![type:video](https://www.youtube.com/embed/vGKDraGFamM) |
+| FENet v2 | dla34    | llamas  | 0.05       | ![type:video](https://www.youtube.com/embed/TNpBmidhChQ) | ![type:video](https://www.youtube.com/embed/Z67DTfoppVo) |
+
+### Multitask Detection Methods
+
+#### YOLOPv2
+
+This work proposes an efficient multi-task learning network for autonomous driving,
+combining traffic object detection, drivable road area segmentation, and lane detection.
+YOLOPv2 model achieves new state-of-the-art performance in accuracy and speed on the BDD100K dataset,
+halving the inference time compared to previous benchmarks.
+
+- **Paper**: [YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception](https://arxiv.org/abs/2208.11434)
+- **Code**: [GitHub](https://github.com/CAIC-AD/YOLOPv2)
+
+| Method  | Campus Video                                             | Road Video                                               |
+| ------- | -------------------------------------------------------- | -------------------------------------------------------- |
+| YOLOPv2 | ![type:video](https://www.youtube.com/embed/iovwTg3cisA) | ![type:video](https://www.youtube.com/embed/UzkCnI0Sx7c) |
+
+#### HybridNets
+
+This work introduces HybridNets, an end-to-end perception network for autonomous driving.
+It optimizes segmentation heads and box/class prediction networks using a weighted bidirectional feature network.
+HybridNets achieves good performance on BDD100K and Berkeley DeepDrive datasets, outperforming state-of-the-art methods.
+
+- **Paper**: [HybridNets: End-to-End Perception Network](https://arxiv.org/abs/2203.09035)
+- **Code**: [GitHub](https://github.com/datvuthanh/HybridNets)
+
+| Method     | Campus Video                                             | Road Video                                               |
+| ---------- | -------------------------------------------------------- | -------------------------------------------------------- |
+| HybridNets | ![type:video](https://www.youtube.com/embed/ph9TKSiWvd4) | ![type:video](https://www.youtube.com/embed/aNsm4Uj1gcA) |
+
+#### TwinLiteNet
+
+This work introduces TwinLiteNet, a lightweight model designed for driveable area and lane line segmentation in
+autonomous driving.
+
+- **Paper
+  **: [TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars](https://arxiv.org/abs/2307.10705)
+- **Code**: [GitHub](https://github.com/chequanghuy/TwinLiteNet)
+
+| Method      | Campus Video                                             | Road Video                                               |
+| ----------- | -------------------------------------------------------- | -------------------------------------------------------- |
+| Twinlitenet | ![type:video](https://www.youtube.com/embed/hDIcbBup7ww) | ![type:video](https://www.youtube.com/embed/4J9zSoVxw-Q) |
+
+## Citation
+
+```bibtex
+@article{honda2023clrernet,
+title={CLRerNet: Improving Confidence of Lane Detection with LaneIoU},
+author={Hiroto Honda and Yusuke Uchida},
+journal={arXiv preprint arXiv:2305.08366},
+year={2023},
+}
+```
+
+```bibtex
+@InProceedings{Zheng_2022_CVPR,
+    author    = {Zheng, Tu and Huang, Yifei and Liu, Yang and Tang, Wenjian and Yang, Zheng and Cai, Deng and He, Xiaofei},
+    title     = {CLRNet: Cross Layer Refinement Network for Lane Detection},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    month     = {June},
+    year      = {2022},
+    pages     = {898-907}
+}
+```
+
+```bibtex
+@article{wang&zhong_2024fenet,
+      title={FENet: Focusing Enhanced Network for Lane Detection},
+      author={Liman Wang and Hanyang Zhong},
+      year={2024},
+      eprint={2312.17163},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+```bibtex
+@misc{vu2022hybridnets,
+      title={HybridNets: End-to-End Perception Network},
+      author={Dat Vu and Bao Ngo and Hung Phan},
+      year={2022},
+      eprint={2203.09035},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+```bibtex
+@INPROCEEDINGS{10288646,
+  author={Che, Quang-Huy and Nguyen, Dinh-Phuc and Pham, Minh-Quan and Lam, Duc-Khai},
+  booktitle={2023 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)},
+  title={TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars},
+  year={2023},
+  volume={},
+  number={},
+  pages={1-6},
+  doi={10.1109/MAPR59823.2023.10288646}
+}
+```