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Related Experiment Video

Updated: Sep 17, 2025

Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation

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Published on: January 6, 2023

A dual-domain perception gate-controlled adaptive fusion algorithm for road crack detection.

Ziyang Zhang¹, Yong'an Feng²

¹Liaoning Technical University, Huludao, 125105, China.

Scientific Reports

|July 2, 2025

Summary

This study introduces a new road crack detection network (DP-DETR) that improves accuracy by focusing on critical features and high-frequency crack details. The advanced dual-domain perception method enhances detection performance for road defects.

Keywords:

Defect detection Dual-domain perception Feature enhancement Gate-controlled adaptive fusion RT-DETR

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Area of Science:

Computer Vision
Artificial Intelligence
Road Infrastructure Maintenance

Background:

Current road crack detection methods struggle with diverse defect patterns and complex anomalies.
Existing object detection algorithms exhibit limitations in feature redundancy management and multi-stage feature fusion.
High-frequency characteristics of crack features are often neglected, leading to inefficient network performance and information loss.

Purpose of the Study:

To propose a novel dual-domain perception gate-controlled adaptive fusion network (DP-DETR) for enhanced road crack detection.
To address deficiencies in feature redundancy, indiscriminate fusion, and neglect of high-frequency crack characteristics in current methods.
To improve the efficiency and accuracy of road defect detection systems.

Main Methods:

Developed a dual-domain dynamic perception information distillation mechanism to reduce redundancy and improve feature representation.
Introduced a feature information gating-adaptive fusion module (FGAF-Fusion) for interactive channel-spatial fusion and selective retention of small-scale target features.
Designed a dual-domain structural feature enhancement loss function to emphasize high-frequency crack information and edge texture features.

Main Results:

The proposed DP-DETR achieved mAP50 of 54.2% and mAP50:95 of 25.8% on the RDD2022 dataset.
Demonstrated significant improvements of 6.7 and 4.2 percentage points over RT-DETR in key performance metrics.
Effectively detected various road defects, showcasing competitive results and robustness in road crack object detection tasks.

Conclusions:

The DP-DETR network offers a robust and efficient solution for road crack detection.
The dual-domain perception and adaptive fusion strategies effectively capture salient crack features.
The proposed method significantly advances the state-of-the-art in automated road defect analysis.