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

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.

随着在自动驾驶，机器人导航和地形映射等领域的光检测和范围技术的广泛应用（LIDAR）技术，LIDAR图像中边缘检测的重要性变得越来越突出。处理激光雷达图像时，传统的边缘检测方法通常会面临准确性和计算复杂性的挑战。为了解决这些问题，本研究提出了一种基于人工智能技术的LIDAR图像的边缘检测方法。本文首先回顾了有关激光雷达技术和图像边缘检测的当前研究状态，引入了共同的边缘检测算法及其在LIDAR图像处理中的应用。随后，设计和实施了基于深度学习的边缘检测模型，通过预处理和增强LIDAR图像数据集来优化模型训练过程。实验结果表明，所提出的方法在检测准确性和计算效率方面优于传统方法，显示出显着的实际应用价值。最后，为当前方法的缺点提出了改进策略，并通过实验验证了这些改进。