  In the construction sector, ensuring worker safety is of the utmost
significance. In this study, a deep learning-based technique is presented for
identifying safety gear worn by construction workers, such as helmets, goggles,
jackets, gloves, and footwears. The recommended approach uses the YOLO v7 (You
Only Look Once) object detection algorithm to precisely locate these safety
items. The dataset utilized in this work consists of labeled images split into
training, testing and validation sets. Each image has bounding box labels that
indicate where the safety equipment is located within the image. The model is
trained to identify and categorize the safety equipment based on the labeled
dataset through an iterative training approach. We used custom dataset to train
this model. Our trained model performed admirably well, with good precision,
recall, and F1-score for safety equipment recognition. Also, the model's
evaluation produced encouraging results, with a<EMAIL_ADDRESS>score of 87.7\%. The
model performs effectively, making it possible to quickly identify safety
equipment violations on building sites. A thorough evaluation of the outcomes
reveals the model's advantages and points up potential areas for development.
By offering an automatic and trustworthy method for safety equipment detection,
this research makes a contribution to the fields of computer vision and
workplace safety. The proposed deep learning-based approach will increase
safety compliance and reduce the risk of accidents in the construction industry
