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Related Experiment Video

Updated: Jul 27, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

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Published on: January 5, 2024

Damage Detection and Localization of Bridge Deck Pavement Based on Deep Learning.

Youhao Ni¹, Jianxiao Mao¹, Yuguang Fu²

¹Key Laboratory of Concrete and Prestressed Concrete Structures of Ministry of Education, Southeast University, Nanjing 210096, China.

Sensors (Basel, Switzerland)

|June 10, 2023

Summary

This summary is machine-generated.

This study introduces a novel three-stage method using YOLOv7 and revised LaneNet for detecting and localizing bridge deck pavement damage. The system achieves high accuracy in identifying road damage and lane lines, enhancing bridge maintenance and safety.

Keywords:

LaneNet YOLOv7 bridge deck pavement damage detection lane line semantic segmentation lane localization of pavement damage

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Area of Science:

Civil Engineering
Computer Vision
Artificial Intelligence

Background:

Bridge deck pavement damage critically impacts driving safety and structural longevity.
Automated detection and localization of such damage are essential for effective bridge maintenance.

Purpose of the Study:

To propose and evaluate a three-stage method for detecting and localizing bridge deck pavement damage.
To enhance the accuracy and efficiency of automated bridge inspection systems.

Main Methods:

A three-stage approach combining YOLOv7 for damage detection and a revised LaneNet for lane line segmentation.
Utilizing the Road Damage Dataset 2022 (RDD2022) for training and validation.
Implementing image processing algorithms for lane area extraction and damage localization.

Main Results:

The YOLOv7 model achieved a mean average precision (mAP) of 0.663 on the RDD2022 dataset.
The revised LaneNet demonstrated a lane localization accuracy of 0.933, outperforming instance segmentation (0.856).
The system achieved an inference speed of 12.3 FPS, significantly faster than instance segmentation (6.53 FPS).

Conclusions:

The proposed method effectively detects and localizes bridge deck pavement damage with high accuracy and speed.
This approach offers a valuable reference for bridge deck pavement maintenance and inspection.
The integration of YOLOv7 and revised LaneNet provides a robust solution for automated bridge health monitoring.