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Lightweight Model for Pavement Defect Detection Based on Improved YOLOv7.

Peile Huang1, Shenghuai Wang1, Jianyu Chen1

  • 1School of Mechanical Engineering, Hubei University of Automotive Technology, Shiyan 442002, China.

Sensors (Basel, Switzerland)
|August 26, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a lightweight pavement defect detection model using an improved YOLOv7 architecture. The enhanced model achieves high accuracy and speed, making it suitable for edge devices.

Keywords:
CBAM convolution moduleGhost Conv moduleK-meansSPPCSPC_GroupYOLOv7defect detectionpavement defect detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Civil Engineering

Background:

  • Existing pavement defect detection models struggle with accuracy, speed, and large parameter sizes.
  • These limitations hinder the deployment of models on edge devices with limited computational power.

Purpose of the Study:

  • To develop a lightweight and efficient pavement defect detection model.
  • To improve the accuracy and speed of pavement defect detection for edge computing applications.

Main Methods:

  • An improved YOLOv7 architecture was proposed, incorporating SPPCSPC_Group, K-means clustering for anchors, Ghost Conv, and CBAM modules.
  • These enhancements aim to reduce parameter load, computational complexity, and improve feature extraction.

Main Results:

  • The improved model achieved 91% average accuracy, with notable increases in detecting broken plates (9%) and repaired areas (8%).
  • Significant reductions in calculations (14.4%), parameters (29.3%), and model size (29.1%) were observed.
  • The model achieved a processing speed of 80 FPS (frames per second).

Conclusions:

  • The enhanced YOLOv7 model effectively balances parameter reduction and computational efficiency while maintaining high detection accuracy.
  • This lightweight model is a suitable alternative for pavement defect detection on resource-constrained edge devices.