Real-Time Deep-Learning-Based Recognition of Helmet-Wearing Personnel on Construction Sites from a Distance

On construction sites, it is crucial and and in most cases mandatory to wear safety equipment such as helmets, safety shoes, vests, and belts. The most important of these is the helmet, as it protects against head injuries and can also serve as a marker for detecting and tracking workers, since a helmet is typically visible to cameras on construction sites. Checking helmet usage, however, is a labor-intensive and time-consuming process. A lot of work has been conducted on detecting and tracking people. Some studies have involved hardware-based systems that require batteries and are often perceived as intrusive by workers, while others have focused on vision-based methods. The aim of this work is not only to detect workers and helmets, but also to identify workers through labeled helmets using symbol detection methods. Person and helmet detection tasks were handled by training existing datasets and gained accurate results. For symbol detection, 14 different shapes were selected and put on helmets in a triple format side by side. A total of 11,243 images have been annotated. YOLOv5 and YOLOv8 were used to train the dataset and obtain models. The results show that both methods achieved high precision and recall. However, YOLOv5 slightly outperformed YOLOv8 in real-time identification tests, correctly detecting the helmet symbols. A testing dataset containing different distances was generated in order to measure accuracy by distance. According to the results, accurate identification was achieved at distances of up to 10 meters. Also, a location-based symbol-ordering algorithm is proposed. Since symbol detection does not follow any order and works with confidence values in the inference mode, a left to right approach is followed.