YOLOv7-Based Deep Learning Model for Detecting Full Safety Gear on Construction Workers

The safety of construction workers is critical aspect of the industry, as a lack of compliance with protective measures

often leads to serious accidents. This research presents a computer vision-based solution that employs deep learning to

automatically recognize personal protective equipment (PPE), such as helmets, gloves, safety jackets, goggles, and protective

footwear. The system is implemented using the YOLOv7 object detection framework, which has been trained on a carefully

prepared custom dataset. Each image in the dataset was annotated with bounding boxes to indicate the position and category of

safety gear. After multiple training cycles, the model demonstrated strong recognition ability across different PPE types.

Evaluation metrics, including precision, recall, F1-score, and mean Average Precision ([email protected]), confirm the effectiveness of

the approach, with the best performance achieving an mAP of 87.7%. These outcomes highlight the potential of the proposed

system to support real-time monitoring of safety compliance on construction sites