SH17: A Dataset for Human Safety and Personal Protective Equipment Detection in Manufacturing Industry

TL;DR

This paper introduces the SH17 dataset and CNN-based object detection models to improve PPE compliance monitoring in industrial workplaces, demonstrating promising accuracy for safety enforcement.

Contribution

The paper presents a new annotated dataset and benchmarks state-of-the-art object detection models for PPE detection in manufacturing environments.

Findings

YOLOv9-e model exceeds 70.9% accuracy in PPE detection

The dataset contains 8,099 images with 75,994 PPE instances

Models show promising cross-domain performance

Abstract

Workplace accidents continue to pose significant risks for human safety, particularly in industries such as construction and manufacturing, and the necessity for effective Personal Protective Equipment (PPE) compliance has become increasingly paramount. Our research focuses on the development of non-invasive techniques based on the Object Detection (OD) and Convolutional Neural Network (CNN) to detect and verify the proper use of various types of PPE such as helmets, safety glasses, masks, and protective clothing. This study proposes the SH17 Dataset, consisting of 8,099 annotated images containing 75,994 instances of 17 classes collected from diverse industrial environments, to train and validate the OD models. We have trained state-of-the-art OD models for benchmarking, and initial results demonstrate promising accuracy levels with You Only Look Once (YOLO)v9-e model variant exceeding 70.9% in PPE detection. The performance of the model validation on cross-domain datasets suggests that integrating these technologies can significantly improve safety management systems, providing a scalable and efficient solution for industries striving to meet human safety regulations and protect their workforce. The dataset is available at https://github.com/ahmadmughees/sh17dataset.