Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Vol 8 No 3 (2024): Volume 8, Issue 3, March 2024 | Pages: 167-172
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 01-04-2024
With the increasing availability of LiDAR and comparable sensor technologies, the accessibility of point cloud data has significantly improved. Nevertheless, the magnitude and intricacy of point cloud data pose challenges for processing and interpretation. A point cloud classification model utilizing deep learning is proposed as a solution to the difficulties encountered in evaluating point cloud data, primarily acquired via LiDAR and comparable sensor technologies. The utilization of data augmentation technologies and meticulous preprocessing enhances the efficiency of the Point-Net model in processing and categorizing point cloud data. Empirical findings validate the significance of optimizing hyperparameters, such as number of epochs, batch size, and learning rate, in order to enhance the performance of the model. The enhanced Point-Net model demonstrated a notable enhancement in classification accuracy, reaching a maximum accuracy of 0.7097, which is a substantial improvement compared to the initial performance.
LiDAR, Deep Learning, Point-Net Network, Fine-Tuning Hyperparameter, Point Cloud Classification
Shahlaa Falah Hasan Al-Tameemi, Alaa Ali Ghaith, Ahmad Ghandour, “Utilization of Deep Learning for LiDAR Point Cloud Classification” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 8, Issue 3, pp 167-172, March 2024. Article DOI https://doi.org/10.47001/IRJIET/2024.803022
This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence