# Grid Anchor Lane Detection Based on Attribute Correlation
## 5 Conclusions  
In this paper, a novel lane line representation method called the grid anchor was proposed. The grid anchor not only corresponds to the feature map structure but also introduces more detailed attributes that specify the lane line's feature associations. To ensure that all positions in the feature map integrate global features, the model includes an MLP for feature propagation from top to bottom layers. Additionally, a convolutional reordering upsampling method was introduced. In the grid anchor representation, to enhance the feature associations between adjacent anchors, an attribute correlation loss function was constructed based on the direction attribute, grounded in the classification and regression loss functions. The model was evaluated on two mainstream lane detection datasets: CULane and Tusimple. The experimental results show that our model achieves first-place accuracy in both normal and curved scenarios, as well as good speed performance. Ablation studies also quantified the accuracy improvement brought by each proposed module. The model still has considerable room for improvement. Each grid anchor's detection result is computed using features from the corresponding location and its neighborhood. However, lane lines exhibit higher feature associations with each other, so using deformable convolutions to calculate the output grid anchor data could further improve the model's performance. Additionally, incorporating a feature post-processing module into the model could further improve its robustness. Grid anchors calculate outputs based on feature maps without requiring additional detection models, making it possible to perform lane detection alongside other object detection tasks. Future research could focus on integrating lane detection and other road object detection using grid anchors, bringing the approach closer to real-world application needs. Additionally, the construction of future datasets should emphasize the collection of samples in extreme scenarios to provide diverse training and testing conditions, thereby promoting model performance improvements in such challenging environments. Conceptualization, Q.F. ; data curation, C.C. and H.W. ; formal analysis, J.S. ; funding acquisition, F.C. and G.X. ; methodology, Q.F. ; project administration, F.C. and G.X. ; resources, C.C., F.C. and J.S. ; software, Q.F. and H.W. ; supervision, C.C., F.C. and G.X. ; validation, J.S. and H.W. ; visualization, C.C. ; writing--original draft, Q.F. ; writing--review and editing, G.X. All authors have read and agreed to the published version of the manuscript. This work was funded by the National Natural Science Foundation of China (52075345), the Department of Education of Guangdong Province (2022 KCXTD027), the Ordinary University Engineering Technology Development Center Project of Guangdong Province (2019 GCZX006), the Key Area Project for Higher Education Institution of Guangdong Province (No. 2023 ZDZX3028), the Guangdong Key Construction Discipline Research Ability Enhancement Project (2021 ZDJS108 and 2022 ZDJS114), and the Self-made Experimental Instruments and Equipment Project of Shenzhen Technology University (JSZZ202301013). The data that support the findings of this study are openly available in CULane at [[http://dx.doi.org/10.48550/arXiv.1712.06080](http://dx.doi.org/10.48550/arXiv.1712.06080)]([http://dx.doi.org/10.48550/arXiv.1712.06080](http://dx.doi.org/10.48550/arXiv.1712.06080)) and Tusimple. These datasets were derived from the following public domain resources: [[https://xingangpan.github.io/projects/CULane.html](https://xingangpan.github.io/projects/CULane.html)]([https://xingangpan.github.io/projects/CULane.html](https://xingangpan.github.io/projects/CULane.html)); [[https://github.com/TuSimple/tusimple-benchmark](https://github.com/TuSimple/tusimple-benchmark)]([https://github.com/TuSimple/tusimple-benchmark](https://github.com/TuSimple/tusimple-benchmark)).