Feature Fusion-Based Deep Learning Model to Ensure Worker Safety at Construction Sites

Safety helmets must be worn at construction to avoid injuries. Manual inspection of safety helmets for every worker requires a lot of human effort which is a costlier and tedious task. Hence, visual inspection can save human effort and reduce the cost. It helps the manager to send safety reminders to the workers. Therefore, this paper describes a feature fusion method to detect safety helmets at construction sites without human intervention. Because of the complex sciences of a construction site, this paper proposed a vibrant deep-learning solution with a better accuracy rate. Here, safety helmets are identified with a feature fusion deep learning model. The features of images are extracted with a hand-crafted deep learning model and pre-trained models that are later fused into a matrix with the fusion technique. Finally, the classification was employed with a convolution neural network. The proposed model was compared with other state-of-the-art deep learning models, and it outperforms with 98.90% correct classification accuracy.