PotholeGuard: A Pothole Detection Approach by Point Cloud Semantic
Segmentation
- URL: http://arxiv.org/abs/2311.02641v1
- Date: Sun, 5 Nov 2023 12:57:05 GMT
- Title: PotholeGuard: A Pothole Detection Approach by Point Cloud Semantic
Segmentation
- Authors: Sahil Nawale, Dhruv Khut, Daksh Dave, Gauransh Sawhney, Pushkar
Aggrawal, Dr. Kailas Devadakar
- Abstract summary: 3D Semantic Pothole research often overlooks point cloud sparsity, leading to suboptimal local feature capture and segmentation accuracy.
Our model efficiently identifies hidden features and uses a feedback mechanism to enhance local characteristics.
Our approach offers a promising solution for robust and accurate 3D pothole segmentation, with applications in road maintenance and safety.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Pothole detection is crucial for road safety and maintenance, traditionally
relying on 2D image segmentation. However, existing 3D Semantic Pothole
Segmentation research often overlooks point cloud sparsity, leading to
suboptimal local feature capture and segmentation accuracy. Our research
presents an innovative point cloud-based pothole segmentation architecture. Our
model efficiently identifies hidden features and uses a feedback mechanism to
enhance local characteristics, improving feature presentation. We introduce a
local relationship learning module to understand local shape relationships,
enhancing structural insights. Additionally, we propose a lightweight adaptive
structure for refining local point features using the K nearest neighbor
algorithm, addressing point cloud density differences and domain selection.
Shared MLP Pooling is integrated to learn deep aggregation features,
facilitating semantic data exploration and segmentation guidance. Extensive
experiments on three public datasets confirm PotholeGuard's superior
performance over state-of-the-art methods. Our approach offers a promising
solution for robust and accurate 3D pothole segmentation, with applications in
road maintenance and safety.
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