Research on Edge Detection of LiDAR Images Based on Artificial Intelligence Technology

TL;DR

This paper introduces a deep learning-based edge detection method for LiDAR images, improving accuracy and efficiency over traditional techniques, with practical applications in autonomous driving and terrain mapping.

Contribution

The study develops a novel AI-driven edge detection model specifically optimized for LiDAR images, enhancing detection performance and computational speed.

Findings

Outperforms traditional edge detection methods in accuracy

Reduces computational complexity in LiDAR image processing

Validated improvements through experimental results

Abstract

With the widespread application of Light Detection and Ranging (LiDAR) technology in fields such as autonomous driving, robot navigation, and terrain mapping, the importance of edge detection in LiDAR images has become increasingly prominent. Traditional edge detection methods often face challenges in accuracy and computational complexity when processing LiDAR images. To address these issues, this study proposes an edge detection method for LiDAR images based on artificial intelligence technology. This paper first reviews the current state of research on LiDAR technology and image edge detection, introducing common edge detection algorithms and their applications in LiDAR image processing. Subsequently, a deep learning-based edge detection model is designed and implemented, optimizing the model training process through preprocessing and enhancement of the LiDAR image dataset. Experimental results indicate that the proposed method outperforms traditional methods in terms of detection accuracy and computational efficiency, showing significant practical application value. Finally, improvement strategies are proposed for the current method's shortcomings, and the improvements are validated through experiments.