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%.

Concrete is known for its a strength and durability as a building material. It is heavily utilized in almost all infrastructures, from pipes, building structures to roads and dams. However,...

This paper presents an application for automatic Vietnamese business cards recognition. Vietnamese business cards are characterized by complex layouts, Vietnamese-English mixed texts, and diverse...