# Unstructured Road Vanishing Point Detection Using the Convolutional Neural Network and Heatmap Regression

arXiv: https://arxiv.org/abs/2006.04691 · HTML: https://mlsanity.com/paper/2006.04691/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [RLGF: Reinforcement Learning with Geometric Feedback for Autonomous Driving Video Generation](https://mlsanity.com/paper/2509.16500/) (2025)
- [End-to-End Monocular Vanishing Point Detection Exploiting Lane Annotations](https://mlsanity.com/paper/2108.13699/) (2021)

## Abstract (arXiv)

Unstructured road vanishing point (VP) detection is a challenging problem, especially in the field of autonomous driving. In this paper, we proposed a novel solution combining the convolutional neural network (CNN) and heatmap regression to detect unstructured road VP. The proposed algorithm firstly adopts a lightweight backbone, i.e., depthwise convolution modified HRNet, to extract hierarchical features of the unstructured road image. Then, three advanced strategies, i.e., multi-scale supervised learning, heatmap super-resolution, and coordinate regression techniques are utilized to achieve fast and high-precision unstructured road VP detection. The empirical results on Kong's dataset show that our proposed approach enjoys the highest detection accuracy compared with state-of-the-art methods under various conditions in real-time, achieving the highest speed of 33 fps.