论文标题
通过优化空间嵌入来朝着轻质车道检测
Towards Lightweight Lane Detection by Optimizing Spatial Embedding
论文作者
论文摘要
许多车道检测方法取决于无提案的实例分割，因为其对柔性对象形状，遮挡和实时应用的适应性。本文解决了一个问题，即基于无提案实例分割的泳道检测中的像素嵌入很难优化。卷积的翻译不变性，这是所谓的优势之一，在优化像素嵌入时会引起挑战。在这项工作中，我们提出了一种基于无建议实例分割的车道检测方法，使用图像坐标直接优化像素的空间嵌入。我们提出的方法允许以端到端方式进行中心定位的后处理步骤，并优化聚类。提出的方法通过后处理的简单性和轻巧的骨干来实现实时车道检测。我们提出的方法证明了公共车道检测数据集的竞争性能。
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.
