# Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety
###### Abstract  
Worker safety at construction sites is a growing concern for many construction industries. Wearing safety helmets can reduce injuries to workers at construction sites, but due to various reasons, safety helmets are not always worn properly. Hence, a computer vision-based automatic safety helmet detection system is extremely important. Many researchers have developed machine and deep learning-based helmet detection systems, but few have focused on helmet detection at construction sites. This paper presents a You Only Look Once (YOLO)-based real-time computer vision-based automatic safety helmet detection system at a construction site. YOLO architecture is high-speed and can process 45 frames per second, making YOLO-based architectures feasible to use in real-time safety helmet detection. A benchmark dataset containing 5000 images of hard hats was used in this study, which was further divided in a ratio of 60:20:20 (%) for training, testing, and validation, respectively. The experimental results showed that the YOLOv5x architecture achieved the best mean average precision (mAP) of 92.44%, thereby showing excellent results in detecting safety helmets even in low-light conditions. computer vision; safety helmet detection; You Only Look Once (YOLO); deep learning +
Footnote †: journal: Applied  
Figure 1 shows the data for the leading types of fatal accidents in the U.K. from 2016 to 2021. Falling, slips, being struck by equipment, electrocution, and getting entangled between equipment were the significant causes of construction site fatalities [8]. Fall-related deaths in construction jobs accounted for 34.6% of overall construction deaths [8], up from 49.9% in the 1980s and the first half of the 1990s [9]. It is critical to monitor construction workers' safety. Protection equipment use monitoring is part of construction site safety management. In most falling accidents, workers fall from heights and smash their heads on hard floors. Safety helmets can absorb and diffuse the impact of falling, reducing the risk of injury to workers who fall from heights. Hard helmets are made to withstand shock, object penetration, and contact with electrical hazards. Half of all fatalities from accidental falls and a considerable number of fatalities from slips, trips, and being struck by falling items may be reduced if employees wore hard helmets properly [10]. Previous studies have shown that wearing a safety helmet can reduce severe brain damage by up to 95% [11]. In order to ensure worker safety, various countries have imposed industrial safety regulations. The U.S. government has created an organization called the Occupational Safety and Health Administration (OSHA), which aims to develop and impose rules and regulations (like wearing a safety helmet, glasses, etc.) at construction sites to reduce injuries. However, for reasons such as simple negligence or misinformation about safety helmets, workers at construction sites do not follow the OSHA guidelines. Manually monitoring violations of safety helmet regulations is not a feasible solution, especially at large construction sites. Therefore, automatic detection of safety helmet use is extremely important. Automatic helmet detection is basically an object detection problem and can be solved using deep learning and computer vision-based approaches. Due to its computational method and precision in the field of object detection, deep learning and its applications in computer vision have achieved a breakthrough [12]. The object identification method has been a research hotspot in the field of computer vision in recent years [13]. Two types of state-of-the-art deep learning methods for object detection are currently available: the R-CNN (Convolutional Neural Network)-based target detection algorithm, which generates candidate regions first and then performs classification or regression [14], and the You Only Look Once [15; 16] (YOLO) and Single Shot MultiBox Detector (SSD) algorithms [17], which perform classification or regression using only one CNN.