Summary of Multi-class Road Defect Detection and Segmentation Using Spatial and Channel-wise Attention For Autonomous Road Repairing, by Jongmin Yu et al.
Multi-class Road Defect Detection and Segmentation using Spatial and Channel-wise Attention for Autonomous Road Repairing
by Jongmin Yu, Chen Bene Chi, Sebastiano Fichera, Paolo Paoletti, Devansh Mehta, Shan Luo
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an end-to-end instance segmentation method for detecting and segmenting multiple defects in road pavement images. The method leverages spatial and channel-wise attention blocks to learn global representations of morphological information, color, and depth features. By simultaneously performing multi-class defect detection and segmentation, the proposed approach aims to improve autonomous road repair systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Roads need to be repaired, and self-driving machines can help! This paper develops a special computer program that can identify different types of problems on roads (like cracks or potholes) and mark them out so they can be fixed. The program uses special tricks called “attention blocks” to understand the patterns in road images and find the defects. It’s like having a superpower for robots! |
Keywords
» Artificial intelligence » Attention » Instance segmentation
