Personal Protective Equipment Detection with YOLOv5s
Computer-vision research comparing activation functions for construction-site safety-equipment detection.
Overview
This project applies object detection to construction-site safety monitoring. A dataset of 2,632 construction images was used to train YOLOv5s models for detecting personal protective equipment.
Method
- Prepared and trained models on the custom construction-site dataset.
- Compared six activation functions under the same YOLOv5s architecture.
- Evaluated each model using precision, recall, mAP@0.5, and mAP@0.5–0.95.
Results
The SiLU configuration achieved 84.8% precision, 80.2% recall, 83.9% mAP@0.5, and 43.6% mAP@0.5–0.95. The work was presented at the 2023 National Conference on Smart Technology Applications in Industry 4.0, Smart City, and Sustainability.