30 lines
1.8 KiB
Markdown
30 lines
1.8 KiB
Markdown
# Project Proposal: Attention-Enhanced 3D Point Cloud Segmentation for Railway Foreign Object Detection
|
|
|
|
## Research Question and Hypothesis
|
|
|
|
**Research Question:** Can attention-based 3D point cloud segmentation architectures significantly improve the detection of rare foreign objects on railway tracks compared to standard RandLA-Net?
|
|
|
|
**Hypothesis:** A hybrid architecture combining Attention mechanisms with RandLA-Net's efficient sampling will achieve at least 65% IoU for foreign object detection, while maintaining computational efficiency suitable for real-time monitoring.
|
|
|
|
## Literature Review
|
|
|
|
Recent advancements in transformer-based architectures like Point Transformer V3 [Wu et al., 2024] have shown superior performance in capturing fine-grained features. Meanwhile, approaches like Sphere Transformer [Lai et al., 2023] specifically address LiDAR's unique distribution patterns. The class imbalance problem has been partially addressed by RAPiD-Seg [Li et al., 2024] through range-aware feature embeddings.
|
|
|
|
## Methodology
|
|
|
|
We will develop a hybrid architecture integrating:
|
|
|
|
1. RandLA-Net's efficient random sampling for computational feasibility
|
|
2. Point Transformer's vector attention mechanisms for capturing fine details
|
|
3. Class-balanced loss functions to address the extreme imbalance (0.001% foreign objects)
|
|
4. Data augmentation techniques including selective jittering [Park et al., 2024]
|
|
|
|
Evaluation will use precision, recall, and IoU metrics, with special emphasis on foreign object performance. Computational efficiency will be benchmarked against RandLA-Net baseline.
|
|
|
|
## Planning
|
|
|
|
- Weeks 1-2: Architecture design and implementation
|
|
- Weeks 3-4: Training and hyperparameter optimization
|
|
- Weeks 5-6: Evaluation and comparison with baseline
|
|
- Weeks 7-8: Documentation and reporting
|