Recent work done on lane detection has been able to detect lanes accurately in complex scenarios, yet many fail to deliver real-time performance specifically with limited computational resources. In this work, we propose SwiftLane: a simple and light-weight, end-to-end deep learning based framework, coupled with the row-wise classification formulation for fast and efficient lane detection. This framework is supplemented with a false positive suppression algorithm and a curve fitting technique to further increase the accuracy. Our method achieves an inference speed of 411 frames per second, surpassing state-of-the-art in terms of speed while achieving comparable results in terms of accuracy on the popular CULane benchmark dataset. In addition, our proposed framework together with TensorRT optimization facilitates real-time lane detection on a Nvidia Jetson AGX Xavier as an embedded system while achieving a high inference speed of 56 frames per second.
@article{arxiv.2110.11779,
title = {SwiftLane: Towards Fast and Efficient Lane Detection},
author = {Oshada Jayasinghe and Damith Anhettigama and Sahan Hemachandra and Shenali Kariyawasam and Ranga Rodrigo and Peshala Jayasekara},
journal= {arXiv preprint arXiv:2110.11779},
year = {2022}
}
Comments
Accepted to 20th IEEE International Conference on Machine Learning and Applications (ICMLA) 2021