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Research on road surface damage detection based on SEA-YOLO v8.

Yuxi Zhao1, Baoyong Shi2, Xiaoguang Duan3

  • 1Jinan Zhuolun Intelligent Transportation Technology Co., LTD, Jinan, Shandong, China.

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Summary
This summary is machine-generated.

This study introduces the SEA-YOLO v8 model for efficient road damage detection. It improves accuracy and real-time performance, aiding traffic safety and road maintenance efforts.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Road Infrastructure Management

Background:

  • Existing road damage detection methods lack accuracy, real-time capabilities, and adaptability.
  • Traffic safety and road maintenance are critical areas impacted by these limitations.

Purpose of the Study:

  • To develop an advanced road damage detection model (SEA-YOLO v8) addressing current technological shortcomings.
  • To enhance detection accuracy, real-time processing, and model adaptability for complex urban environments.

Main Methods:

  • Constructed the SBS module to optimize computational complexity and reduce model parameters for a lightweight design.
  • Integrated the EMA attention mechanism into the neck component for improved feature representation and selective focus.
  • Proposed an adaptive attention feature pyramid structure to enhance feature fusion capabilities.
  • Introduced the lightweight shared convolutional detection head (LSCD-Head) to further refine feature representation and reduce parameters.

Main Results:

  • The SEA-YOLO v8 model achieved 63.2% mAP50 on the RDD2022 dataset.
  • Demonstrated superior performance compared to the standard YOLOv8 model and other mainstream target detection models.
  • Validated high detection accuracy and adaptability in complex urban traffic scenarios.

Conclusions:

  • The SEA-YOLO v8 model offers accurate road damage localization and detection.
  • The model contributes to saving manpower and material resources in road assessment and maintenance.
  • Promotes sustainable urban road development through improved infrastructure management.