Lane Detection Based on Two-Stage Noise Features Filtering and Clustering

Lane detection is essential for autonomous vehicles, and vision-based lane detection is widely used in the field of intelligent driving cars because of its low cost. Aiming to solve the problem of false detection caused by surrounding vehicles during lane detection, a new multi-lane detection method with strong anti-interference ability is proposed. The main contribution of this work is the utilization of object detection neural network which is applied to remove vehicles from road images as much as possible. Compared with the traditional feature-based detection algorithms, this method can preserve lane line features more accurately and effectively. Firstly, binarization of global optimal threshold method is utilized to extract the basic features of lanes. In order to further remove non-lane line noises and extract lane features as clear as possible, YOLOv4-tiny object detection network is introduced to detect and filter vehicles on the road. Then the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm with denoising function is applied to cluster the different lane features. Finally, an improved RANSAC (Random Sample Consensus) method is used to perform quadratic curve fitting on the clustered features. The experimental results on dataset show that consumed time of a single frame image is only 0.119 second and the precision of the proposed method can reach 94.4%. Compared with other detection algorithms, the proposed algorithm has good real-time performance, stability and robustness in a variety of complex scenarios.

Automated summary

This paper presents a new multi-lane detection method that utilizes an object detection neural network to remove vehicles from road images and accurately extract lane line features. The method utilizes binarization, object detection, and clustering to perform lane detection and demonstrates good real-time performance and robustness in various complex scenarios.