Towards Lightweight Lane Detection by Optimizing Spatial Embedding

• URL: http://arxiv.org/abs/2008.08311v2
• Date: Thu, 27 Aug 2020 06:45:20 GMT
• Title: Towards Lightweight Lane Detection by Optimizing Spatial Embedding
• Authors: Seokwoo Jung, Sungha Choi, Mohammad Azam Khan, Jaegul Choo
• Abstract summary: pixel embedding in proposal-free instance segmentation based lane detection is difficult to optimize. We propose a lane detection method based on proposal-free instance segmentation, directly optimizing spatial embedding of pixels using image coordinate. The proposed method enables real-time lane detection through the simplicity of post-processing and the adoption of a lightweight backbone.
• Score: 31.26216243950601
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A number of lane detection methods depend on a proposal-free instance segmentation because of its adaptability to flexible object shape, occlusion, and real-time application. This paper addresses the problem that pixel embedding in proposal-free instance segmentation based lane detection is difficult to optimize. A translation invariance of convolution, which is one of the supposed strengths, causes challenges in optimizing pixel embedding. In this work, we propose a lane detection method based on proposal-free instance segmentation, directly optimizing spatial embedding of pixels using image coordinate. Our proposed method allows the post-processing step for center localization and optimizes clustering in an end-to-end manner. The proposed method enables real-time lane detection through the simplicity of post-processing and the adoption of a lightweight backbone. Our proposed method demonstrates competitive performance on public lane detection datasets.

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