Prior lane detection with large vision model

Huawei Ba

doi:10.1117/12.3009384

11 October 2023 Prior lane detection with large vision model

Huawei Ba

Proceedings Volume 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023); 1291821 (2023) https://doi.org/10.1117/12.3009384
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2023), 2023, Wuhan, China

Abstract

Lane detection, a fundamental component of autonomous driving, has recently gained significant traction in research and development. Recently, there are lots of methods introduced to improve the performance of detection by designing complex network. However, they ignore the potential application of the semantic parsing ability of large vision models in lane detection. In this paper, we propose a novel and simple detection approach, named Prior Lane Detection (PLD), to perform precisely detection. To be specific, our approach involves utilizing a large vision model to extract semantic proposals as prior information. The large vision model, trained on a vast number of images, exhibits robust zero-shot performance and possesses the ability to generate high-quality object masks. Then, we apply image and semantic proposals to train the detection network with the aim of improving its performance. Our PLD approach utilizes prior semantic information extracted from a large vision model to guide the network’s attention towards important regions during lane detection. The experiments show that leveraging the large vision model can enhance the performance of lane detection models. The results of the experiments prove the usefulness of the proposed PLD on the TuSimple dataset.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Huawei Ba "Prior lane detection with large vision model", Proc. SPIE 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023), 1291821 (11 October 2023); https://doi.org/10.1117/12.3009384

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KEYWORDS

Visual process modeling

Semantics

Image segmentation

Performance modeling

Data modeling

Education and training

Roads

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