The construction sector is one of the most unpredictable and hazardous industry sectors. Tens of millions of construction industry accidents occur globally causing damages and accidents to employees every year. This industry makes up one of the primary sectors of the workforce and activity in the international market is taken into consideration as a vital element in operating the financial system of the country. Construction sites remain dynamic and complex structures. The complex motion and interaction of humans, goods, and power traditionally make construction safety management extremely hard. However, many studies have been conducted in the last decade to introduce innovative technologies for the implementation of efficient protection systems within the construction industry. This project focuses on developing a system using YOLOv7 (You Only Look Once version 7) for the detection of safety products worn by construction workers. The system aims to enhance safety on construction sites by automatically identifying whether workers are wearing essential safety equipment, including hard hats, safety vests, gloves, and more. By leveraging computer vision and deep learning techniques, the project aims to provide an efficient and accurate solution to promote and enforce safety protocols in the construction industry.