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Related Experiment Videos

An Improved YOLOv8 Model for Pavement Distress Detection Under Low-Computing-Power Conditions.

Yi Tang1, Ziyi Yang2, Zhoucong Xu3

  • 1China Merchants Expressway Network & Technology Holdings Co., Ltd., Beijing 100029, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

A new YOLOv8-PDD model enhances automated pavement distress detection (PDD) for infrastructure health monitoring. It achieves higher accuracy with minimal resource increase, offering a better trade-off for real-time applications.

Keywords:
YOLOv8multi-objective detectionpavement distress detectionreal-timeroutine survey

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Civil Engineering

Background:

  • Automated pavement distress detection (PDD) is vital for structural health monitoring (SHM) of transportation infrastructure.
  • Existing PDD methods face challenges in real-time multi-target detection under resource constraints.

Purpose of the Study:

  • To develop an efficient and accurate automated pavement distress detection model for real-time applications.
  • To improve the accuracy-efficiency trade-off in pavement distress detection systems.

Main Methods:

  • Constructed YOLOv8-PDD by integrating the large separable kernel attention (LSKA) mechanism into the Spatial Pyramid Pooling-Fast (SPPF) module.
  • Replaced Complete-IoU (CIoU) loss with Distance-IoU (DIOU) loss and adopted Soft-Non-Maximum Suppression (NMS) over the original NMS algorithm.
  • Evaluated the model's performance on pavement distress detection tasks.

Main Results:

  • YOLOv8-PDD achieved 78.3% mean average precision (mAP@0.5), an 8.1% improvement over the baseline YOLOv8n, with a minor 0.2 GFLOPs complexity increase.
  • The model demonstrated a negligible latency increase (+0.09 ms) while significantly enhancing detection accuracy.
  • Achieved 68 FPS on consumer-grade CPUs (OpenVINO-optimized), outperforming 10 other models in accuracy-speed balance, with notable improvements in pothole and strip patch detection.

Conclusions:

  • YOLOv8-PDD offers a superior accuracy-efficiency trade-off for real-time pavement distress detection.
  • The model exhibits strong adaptability for detecting small targets and complex backgrounds, crucial for infrastructure monitoring.
  • The proposed enhancements provide a promising solution for efficient and accurate structural health monitoring of transportation infrastructure.