Research on Edge Detection of LiDAR Images Based on Artificial Intelligence Technology
This addresses accuracy and efficiency challenges in LiDAR image processing for applications like autonomous driving, though it appears incremental as it builds on existing deep learning techniques.
The study tackled edge detection in LiDAR images by proposing a deep learning-based method, which outperformed traditional approaches in accuracy and computational efficiency, as indicated by experimental results.
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.