  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.
