SwiftLane: Towards Fast and Efficient Lane Detection
| Autor: | Jayasinghe, Oshada, Anhettigama, Damith, Hemachandra, Sahan, Kariyawasam, Shenali, Rodrigo, Ranga, Jayasekara, Peshala |
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| Rok vydání: | 2021 |
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| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/ICMLA52953.2021.00142 |
| Popis: | 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. Comment: Accepted to 20th IEEE International Conference on Machine Learning and Applications (ICMLA) 2021 |
| Databáze: | arXiv |
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