LiDAR-based Object Detection with Real-time Voice Specifications
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916673496285184 |
|---|---|
| author | Kulkarni, Anurag |
| author_facet | Kulkarni, Anurag |
| contents | This paper presents a LiDAR-based object detection system with real-time voice specifications, integrating KITTI's 3D point clouds and RGB images through a multi-modal PointNet framework. It achieves 87.0% validation accuracy on a 3000-sample subset, surpassing a 200-sample baseline of 67.5% by combining spatial and visual data, addressing class imbalance with weighted loss, and refining training via adaptive techniques. A Tkinter prototype provides natural Indian male voice output using Edge TTS (en-IN-PrabhatNeural), alongside 3D visualizations and real-time feedback, enhancing accessibility and safety in autonomous navigation, assistive technology, and beyond. The study offers a detailed methodology, comprehensive experimental analysis, and a broad review of applications and challenges, establishing this work as a scalable advancement in human-computer interaction and environmental perception, aligned with current research trends. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02920 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LiDAR-based Object Detection with Real-time Voice Specifications Kulkarni, Anurag Computer Vision and Pattern Recognition Machine Learning This paper presents a LiDAR-based object detection system with real-time voice specifications, integrating KITTI's 3D point clouds and RGB images through a multi-modal PointNet framework. It achieves 87.0% validation accuracy on a 3000-sample subset, surpassing a 200-sample baseline of 67.5% by combining spatial and visual data, addressing class imbalance with weighted loss, and refining training via adaptive techniques. A Tkinter prototype provides natural Indian male voice output using Edge TTS (en-IN-PrabhatNeural), alongside 3D visualizations and real-time feedback, enhancing accessibility and safety in autonomous navigation, assistive technology, and beyond. The study offers a detailed methodology, comprehensive experimental analysis, and a broad review of applications and challenges, establishing this work as a scalable advancement in human-computer interaction and environmental perception, aligned with current research trends. |
| title | LiDAR-based Object Detection with Real-time Voice Specifications |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2504.02920 |