Partially Visible Lane Detection with Hierarchical Supervision Approach

Engineering notes

Convolution Neural Network (CNN) has exhibited its potential to devise state-of-the-art solutions for various computer vision problems and lane detection.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

ABSTRACT Advanced Driver Assistant System(ADAS) is a critical component of self-driving cars, and lane detection is one of its applications. Convolution Neural Network (CNN) has exhibited its potential to devise state-of-the-art solutions for various computer vision problems and lane detection. However, different scales of lane lines, partial visibility of lane, and the varying resolution of hierarchical features challenge the CNN-based detection methods to localize the lane accurately. In this work, we propose a deep supervision-based model to detect the partially visible lane lines. Hierarchical (Deep) supervision helps the proposed model to trace and classify the lane features at different scales. The simulation of the proposed model gives robust results with partial visibility of lane paint compared to an existing and proven model. The proposed model's accuracy is as high as 92% for partially visible lane and 99% for full visible lane, which stands to the right in its peer work comparison.

Links and sources

• Official / arXiv page

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