Towards Lightweight Lane Detection by Optimizing Spatial Embedding
This work addresses the problem of real-time lane detection for autonomous driving systems, but it is incremental as it builds on existing proposal-free instance segmentation methods.
The paper tackled the difficulty of optimizing pixel embedding in proposal-free instance segmentation for lane detection, which is caused by the translation invariance of convolution, by proposing a method that directly optimizes spatial embedding using image coordinates and simplifies post-processing. The result is a lightweight lane detection method that achieves competitive performance on public datasets.
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.