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This study introduces Platform Safety Detection YOLO (PSD-YOLO), an AI framework enhancing offshore platform safety. PSD-YOLO improves real-time detection of personnel in hazardous environments, reducing accident risks.

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Industrial Safety Engineering

Background:

  • Offshore drilling platforms present complex safety challenges due to hazardous environments.
  • Existing monitoring systems often struggle with real-time detection of personnel in cluttered or occluded conditions.

Purpose of the Study:

  • To develop an enhanced, real-time object detection framework for improving personnel safety on offshore platforms.
  • To address limitations in detecting small, occluded, or dense target scenarios.

Main Methods:

  • Integration of a Channel Attention-Aware (CAA) mechanism to reduce background noise.
  • Introduction of a C2fCIB_Conv2Former module for improved multi-scale feature fusion.
  • Utilization of the Soft-Non-Maximum Suppression (Soft-NMS) algorithm to minimize missed detections.

Main Results:

  • The Platform Safety Detection YOLO (PSD-YOLO) framework achieved a mean Average Precision (mAP@0.5) of 82.5%.
  • The system demonstrated a high inference speed of 232.56 Frames Per Second (FPS).
  • Significant reduction in missed detections in dense scenes was observed compared to traditional methods.

Conclusions:

  • PSD-YOLO offers an efficient and accurate solution for automated safety monitoring in offshore environments.
  • The framework provides critical technical support for real-time hazard warnings, enhancing personnel safety.
  • This advancement contributes to intelligent sensor monitoring and overall safety system improvements for offshore platforms.