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Optimized YOLOv8 framework for intelligent rockfall detection on mountain roads.

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  • 1School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Shaanxi, 710021, China.

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This study introduces Yolov8-GCB, an improved rockfall detection system for roads. It enhances real-time detection on embedded devices, improving safety in remote areas.

Keywords:
Embedded systemObject detectionRoad segmentationRockfall detectionYolov8

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Area of Science:

  • Computer Vision and Machine Learning
  • Geohazard Monitoring
  • Embedded Systems

Background:

  • Rockfalls on mountainous roads present significant safety risks, especially in remote areas with limited communication.
  • Existing detection systems may struggle with efficiency and real-time performance on resource-constrained embedded devices.

Purpose of the Study:

  • To develop an efficient and accurate rockfall detection system for embedded devices.
  • To improve the performance of the Yolov8 algorithm for real-time rockfall detection.

Main Methods:

  • Proposed an improved Yolov8 algorithm (Yolov8-GCB) incorporating a DeepLabv3+ road segmentation module.
  • Replaced standard convolutions with Ghost convolutions for reduced parameters and increased inference speed.
  • Integrated Channel Priori Convolution Attention (CPCA) and enhanced feature extraction in the Neck network.

Main Results:

  • Yolov8-GCB achieved improved detection accuracy (AP@0.5: +1.2%, AP@0.75: +1%).
  • Reduced model parameters by 14.1% and GFLOPs by 16.1%.
  • Increased inference speed by 20.65% compared to the baseline.

Conclusions:

  • The Yolov8-GCB algorithm offers an effective technological solution for real-time rockfall detection on embedded systems.
  • The method is adaptable for detecting other geohazards like landslides and debris flows in infrastructure-limited regions.