SwiftLane: Towards Fast and Efficient Lane Detection

• URL: http://arxiv.org/abs/2110.11779v1
• Date: Fri, 22 Oct 2021 13:35:05 GMT
• Title: SwiftLane: Towards Fast and Efficient Lane Detection
• Authors: Oshada Jayasinghe, Damith Anhettigama, Sahan Hemachandra, Shenali Kariyawasam, Ranga Rodrigo, Peshala Jayasekara
• Abstract summary: We propose SwiftLane: a light-weight, end-to-end deep learning based framework, coupled with the row-wise classification formulation for fast and efficient lane detection. 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.
• Score: 0.8972186395640678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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