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Applied Sciences· 2026Q2

Improved YOLOv11n-Based PPE and Tool Object Detection for Power-Construction Safety Monitoring

Yimang Li, Guyue Hu, Jin Liu, Xilong Lu

Short summary

An improved YOLOv11n model with ECA/SGE attention, SimSPPF, and MPDIoU loss achieves 84.7% mAP@0.5 and 56.6% mAP@0.5:0.95 on a power-construction dataset for detecting 7 PPE and tool classes.

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Key points

  • An improved YOLOv11n model incorporates ECA/SGE attention, SimSPPF, and MPDIoU loss.
  • The model detects 7 object classes: helmet, person, insulating gloves, safety belt, operating rod, voltage tester, and work uniform.
  • Performance metrics on the power-construction dataset are 84.7% mAP@0.5 and 56.6% mAP@0.5:0.95.
  • The system identifies PPE and tools, not direct safety violations.

AI-generated from the title and abstract; the full text is not read.

Abstract

Aiming at the challenges of detecting personal protective equipment (PPE) and tools in power-construction scenes, including missed small objects, confusion between similar objects, inaccurate localization of pose-related objects, reduced robustness in complex backgrounds, and edge-device deployment constraints, this paper proposes an improved YOLOv11n object-detection model. The model embeds ECA and SGE attention mechanisms, replaces the baseline SPPF block with SimSPPF, and introduces the MPDIoU loss function. The resulting detector identifies seven object classes (helmet, person, insulating gloves, safety belt, operating rod, voltage tester, and work uniform); it does not directly classify violation behaviors. After integration of all modules, mAP@0.5 reaches 84.7% and mAP@0.5:0.95 reaches 56.6% on the power-construction dataset. The detected PPE and tool objects can serve as inputs to a subsequent rule layer for safety-violation judgment.

The authors' abstract, as published at the source. Applied Sciences, 2026 · DOI ↗

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