Authors: Yongshang Li, Ronggui Ma, Han Liu, Gaoli Cheng

Published on: July 01, 2023

Impact Score: 8.15

Arxiv code: Arxiv:2307.0027

Summary

• What is new: HrSegNet introduces a new high-resolution network designed specifically for crack segmentation that balances detail preservation with real-time processing speeds.
• Why this is important: Existing models for crack segmentation either lack specialization for the task or struggle to combine high-resolution detail with real-time detection speeds.
• What the research proposes: The HrSegNet model, which utilizes a high-resolution network with semantic guidance to achieve both detailed crack segmentation and real-time inference speeds.
• Results: On the CrackSeg9k, Asphalt3k, and Concrete3k datasets, HrSegNet achieved state-of-the-art performance, outperforming existing models in both segmentation quality and processing speed.

Technical Details

Technological frameworks used: High-resolution convolution neural networks with semantic guidance

Models used: HrSegNet

Data used: CrackSeg9k, Asphalt3k, Concrete3k

Potential Impact

This breakthrough could disrupt the civil engineering and infrastructure maintenance markets, offering new capabilities for companies specializing in structural health monitoring and repair.

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