A Highly Robust Helmet Detection Algorithm Based on YOLO V8 and Transformer

The use of helmets is crucial for safeguarding the lives of construction workers. In the construction sector, computer vision technology is extensively employed to detect and monitor the correct usage of helmets by workers. Currently, there are three classical types of helmet detection algorithms: digital image processing, convolutional neural network (CNN), and Transformer. Digital images are based on manual processing of the features, which proves to be inefficient and lacks robustness. CNN exhibits high accuracy but lacks robustness, which limits its effectiveness in complex environments. This paper proposes an algorithm called the Highly Robust Helmet Detection Algorithm (HRHD), designed to attain precise detection of helmet usage at construction sites with varying conditions. The proposed model leverages the YOLO v8s architecture and incorporates the Coordinate Attention module to enhance the model's focus on important features. It also introduces the Transformer structure to extract global features, and employs the RepConv module to diminish the model's computational demands, thus achieving a balance between inference speed and detection accuracy. The experiments demonstrate that the proposed model in this paper significantly improves the accuracy compared to YOLO v10 and YOLO v8s. Additionally, the model maintains a rapid inference rate, suggesting substantial potential for application within the construction engineering domain.