Related Experiment Video
Updated: Jun 27, 2026
04:58
Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
Published on: January 6, 2023
YOLO-Crack: Geometry-Guided Real-Time Crack Detection Framework Toward Edge Deployment
Zhe Wei1, Rui Wang1, Rong Dai1
1School of Computer Science and Artificial Intelligence, Civil Aviation Flight University of China, Guanghan 618307, China.
Sensors (Basel, Switzerland)
|June 26, 2026
Summary
This study introduces YOLO-Crack, an efficient object detection system for mobile crack inspection. It enhances real-time performance on edge devices by optimizing for slender crack geometry.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Robotics
Background:
- Mobile crack detection faces challenges with slender targets and real-time edge processing.
- General object detectors struggle with these specific constraints.
Purpose of the Study:
- To develop an efficient, closed-loop solution for mobile crack detection on edge devices.
- To improve accuracy and real-time performance for slender crack identification.
Main Methods:
- Proposed YOLO-Crack, integrating geometry-statistics-driven module design.
- Introduced crack-aware cross-dimensional fusion attention (CFCA) and dual-path feature enhancement (DFEM) modules.
- Implemented empirical smooth quality window adjustment with shape consistency regularization for bounding-box regression.
Main Results:
- YOLO-Crack achieved 78.8% precision, 51.4% recall, and 65.7% mAP@0.5 on the Crack500 dataset.
- Outperformed YOLOv11n baseline by 4.2% in precision, 1.7% in recall, and 2.9% in mAP@0.5.
- Achieved 25.5 FPS on a Jetson Orin NX robot, meeting real-time requirements.
Conclusions:
- YOLO-Crack offers a practical workflow for edge vision tasks, from geometric analysis to engineering verification.
- The framework effectively addresses limitations of general detectors for mobile crack inspection.
- Demonstrated successful real-time deployment and validation on a mobile robot platform.
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