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Related Experiment Video

Updated: Sep 13, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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An efficient fusion detector for road defect detection.

Li Yang1,2, Jingwei Deng3, Hailong Duan3,4

  • 1School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China. yangli@tute.edu.cn.

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Summary

This study introduces a new deep learning model, the SCB-AF-Detector, for detecting subtle road defects in complex images. The novel approach effectively identifies multi-scale defects, improving road maintenance accuracy.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Deep learning networks face challenges in detecting multi-scale subtle defects in road images due to complex backgrounds and disappearing fine features.
  • Extracting these fine features is crucial for accurate road defect detection and maintenance.

Purpose of the Study:

  • To propose an effective deep learning model for detecting multi-scale subtle defects in road images with complex backgrounds.
  • To enhance the extraction and fusion of subtle and distant defect features.

Main Methods:

  • Developed the SCB-AF-Detector, integrating space-to-depth convolution (SPD-Conv) with a bottleneck transformer in the SCB-Darknet53 backbone.
  • Employed an enhanced asymptotic feature pyramid network for feature fusion, preserving shallow semantic features and extracting deep semantic features.
  • Utilized the Iran Road Disease Dataset (IRRDD) comprising 25,000 road images for experimentation.

Main Results:

  • The SCB-AF-Detector achieved 90.8% Precision, 95% Recall, and 75.2% mAP (mean Average Precision) on the Iнтэрnet Road Disease Dataset.
  • Demonstrated superior performance in classifying and detecting multi-scale subtle defects in challenging road conditions.

Conclusions:

  • The proposed SCB-AF-Detector effectively addresses the limitations of existing methods in detecting subtle road defects.
  • The model meets high-precision detection requirements for road defects, paving the way for improved road infrastructure monitoring.