SH17: A dataset for human safety and personal protective equipment detection in manufacturing industry

Abstract

Workplace accidents continue to pose significant human safety risks, particularly in the construction and manufacturing industries. The necessity for effective Personal Protective Equipment (PPE) compliance has become increasingly paramount. We focus on developing non-invasive techniques based on the Object Detection (OD) and Convolutional Neural Network (CNN). The aim is 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 validation of the model across cross-domain datasets indicates that integrating these technologies can substantially enhance safety management systems. This approach offers a scalable and efficient solution for industries seeking to comply with human safety regulations while safeguarding their workforce. The dataset is available at https://github.com/ahmadmughees/sh17dataset.