Unsafe Construction Behavior Classification Using Deep Convolutional Neural Network

In the construction industry, about 80-90% of accidents are caused by the unsafe actions and behaviors of employees. Thus, behavior management plays a key role in enhancing safety. In particular, behavior observation is the most critical element for modifying workers' behavior in a safe manner. However, there is a lack of practical methods to measure workers' behavior in construction as current literature only focuses on a few unusual signs such as not wearing personal protective equipment. This paper proposes a system for recognizing workers' dangerous behaviors. To that end, an image dataset has been collected, labeled for three such behaviors. Based on the dataset obtained, the transfer-learning approach is used with three pre-trained models, VGG19, Inception_V3 and InceptionResnet_V2. The results indicate that InceptionResnet_V2 performs better than VGG19_ and Inception_V3 for classifying unsafe behaviors and after 150 epochs, its accuracy reaches 92.44%.