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Updated: Jun 24, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Innovative road distress detection (IR-DD): an efficient and scalable deep learning approach.

Ahsan Zaman Awan¹, Jiancheng Charles Ji², Muhammad Uzair¹

¹School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.

Peerj. Computer Science

|June 10, 2024

Summary

This study introduces Innovative Road Distress Detection (IR-DD), a new framework using YOLOv8 for accurate, real-time road distress detection. It offers a cost-effective alternative to traditional methods for smart cities and autonomous vehicles.

Keywords:

BiFPN Deep learning Efficiency Feature fusion Road distress detection YOLOv8

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Last Updated: Jun 24, 2025

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Transportation infrastructure
Computer vision
Artificial intelligence

Background:

Traditional road distress detection methods are manual, costly, and struggle with diverse surfaces and low-resolution data.
Efficient and accurate road distress detection is vital for safe and reliable transportation systems.
Existing automated methods face challenges with information loss and gradient issues.

Purpose of the Study:

To introduce a novel framework, Innovative Road Distress Detection (IR-DD), for enhanced road distress detection.
To improve accuracy and real-time capabilities in road distress detection for smart city and autonomous vehicle applications.
To address limitations of traditional methods by optimizing multi-scale feature utilization.

Main Methods:

Integration of the YOLOv8 object detection algorithm.
Implementation of bidirectional feature pyramid network (BiFPN) for recursive feature fusion.
Utilization of bidirectional connections to optimize multi-scale feature utilization and mitigate information loss.

Main Results:

The IR-DD framework demonstrates superior performance, efficiency, and robustness compared to conventional methods.
Achieved a precision of 0.666, F1 score of 0.630, and mAP@0.5 of 0.650.
Operates at a high speed of 86 frames per second (FPS), enabling effective real-time detection.

Conclusions:

The IR-DD framework offers a cost-effective and compelling alternative for road distress detection.
The approach significantly advances object detection techniques for practical road infrastructure monitoring.
The findings highlight the effectiveness of IR-DD in enhancing the safety and reliability of transportation systems.