A Hybrid Global-Local Perception Network for Lane Detection
Authors: Qing Chang, Yifei Tong
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our proposed method improves detection accuracy in various challenging scenarios, outperforming the state-of-the-art lane detection methods. |
| Researcher Affiliation | Academia | School of Mechanical Engineering, Nanjing University of Science and Technology, China qingchang@njust.edu.cn, tyf51129@aliyun.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly provide access to its source code via a link or statement of availability. |
| Open Datasets | Yes | We conduct experiments on three widely used benchmarks: CULane (Pan et al. 2018), LLAMAS (Behrendt and Soussan 2019), and Tu Simple (Tu Simple 2020). |
| Dataset Splits | No | The paper specifies training and testing image counts for CULane (88k training, 34k testing) and Tu Simple (3,626 training, 2,782 testing). While it mentions a "valid set" for LLAMAS, it does not provide specific split sizes (e.g., counts or percentages) for a distinct validation set across all datasets to reproduce the data partitioning. |
| Hardware Specification | Yes | Training and testing are both performed on Pytorch with one Tesla-V100 GPU. |
| Software Dependencies | No | The paper mentions "Pytorch" as the framework used, but does not provide specific version numbers for Pytorch or any other software dependencies. |
| Experiment Setup | Yes | All input images are resized to 320 800 during the training and testing phases. In the training process, we use Adam W (Loshchilov and Hutter 2017) optimizer with an initial learning rate of 1e-3 and cosine decay learning rate strategy (Loshchilov and Hutter 2016) with power set to 0.9. For the lane prior head, we set the number of lane prior proposals Np = 192, proposals feature dimension Cp = 64, and the number of points of each lane prior N = 72. For the global extraction head, the resized Hg, Wg are 10, 25, respectively, the channel Cg = 192 and reference points K = 4. The dilation rate in MHA is set as d = 2. The training numbers of epochs for CULane, Tusimple, and LLAMAS are 15, 80, and 20. |