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A Real-Time Mobile Robotic System for Crack Detection in Construction Using Two-Stage Deep Learning.
Emmanuella Ogun1, Yong Ann Voeurn2, Doyun Lee2
1Mechanical Engineering Department, Georgia Southern University, Statesboro, GA 30460, USA.
This study introduces a real-time robotic system for infrastructure inspection, using deep learning for automated crack detection and autonomous navigation. The system successfully identifies micro-cracks, enhancing public safety and inspection efficiency.
Area of Science:
- Civil Engineering
- Robotics
- Computer Vision
- Artificial Intelligence
Background:
- Civil infrastructure deterioration presents significant public safety risks.
- Manual inspections are subjective, labor-intensive, and limited by accessibility.
- Automated inspection systems are needed to overcome these limitations.
Purpose of the Study:
- To develop and evaluate a real-time robotic inspection system integrating deep learning and autonomous navigation.
- To enable simultaneous automated crack detection and collision-free navigation for infrastructure assessment.
- To improve the efficiency, accuracy, and safety of civil infrastructure inspections.
Main Methods:
- A two-stage deep learning neural network: U-Net for segmentation and Pix2Pix conditional generative adversarial network (GAN) for refinement.
- Adversarial residual learning to enhance boundary accuracy and reduce false positives.
- Deployment on an Unmanned Ground Vehicle (UGV) with RGB-D camera and LiDAR for perception and navigation.
Main Results:
- The two-stage model achieved a mean Intersection over Union (mIoU) of 73.9 ± 0.6% and an F1-score of 76.4 ± 0.3% on the CrackSeg9k dataset.
- The robotic system successfully detected micro-cracks as small as 0.3 mm in various validation tests.
- Demonstrated robust performance in simulation, laboratory experiments, and real-world campus hallway tests.
Conclusions:
- The proposed robotic system offers a robust, autonomous solution for field-deployable infrastructure inspection.
- Deep learning integration significantly enhances automated crack detection capabilities.
- The system has the potential to revolutionize infrastructure monitoring and maintenance practices.

