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Related Experiment Video

Updated: Feb 22, 2026

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

Published on: January 6, 2023

A real-time detection framework for road cracks in noisy and morphologically complex environments.

Luxin Fan¹, SaiHong Tang², Mohd Khairol Anuar B Mohd Ariffin³

¹Faculty of Engineering, Universiti Putra Malaysia UPM, Serdang, 43400, Selangor, Malaysia. gs59924@student.upm.edu.my.

Scientific Reports

|February 20, 2026

Summary

This study introduces Crack-YOLO, a fast and accurate road crack detection system for intelligent infrastructure maintenance. The lightweight model significantly improves detection performance in complex environments, outperforming existing methods.

Keywords:

Deep learning Road crack detection YOLOv8

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Area of Science:

Computer Vision
Artificial Intelligence
Civil Engineering

Background:

Automated road defect detection is crucial for intelligent traffic infrastructure maintenance.
Existing object detection models struggle with slow inference speeds and low accuracy, especially in complex environments with shadows, oil stains, and occlusion.
Current models exhibit a sharp decline in detection accuracy under challenging natural conditions.

Purpose of the Study:

To propose a lightweight and high-accuracy road crack detection framework named Crack-YOLO.
To address the limitations of slow speed and low accuracy in existing road defect detection systems.
To enhance detection performance in complex environmental conditions.

Main Methods:

Developed Crack-YOLO, a novel framework based on YOLOv8s.
Replaced original convolution modules with Context-Guided (CG) modules.
Implemented C2f_DynamicConv to substitute static convolution kernels.
Introduced an Adaptive Spatial Feature Fusion (ASFF) head to replace the original detection head.

Main Results:

Crack-YOLO demonstrated superior detection speed and accuracy compared to YOLOv8s across four datasets (CrackVariety, CrackTree200, Crack500, CFD).
Achieved 71.4% mAP@0.5 on the CrackVariety dataset with an inference speed of 416 FPS.
Showcased a 31.0% increase in accuracy and nearly a two-fold increase in speed compared to the baseline YOLOv8s model.
Successfully deployed on a Raspberry Pi 5 edge device for real-time Pavement Condition Index (PCI) calculation.

Conclusions:

Crack-YOLO offers a practical solution for efficient and accurate road defect detection, even on resource-constrained edge devices.
The framework's ability to maintain high performance in complex environments marks a significant advancement in intelligent traffic infrastructure maintenance.
Integration with standards like ASTM D6433 enables automated PCI calculation, demonstrating real-world applicability.