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 (mAP@0.5), 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
M Vyshnavi (Mon,) studied this question.