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Pattern Recognition and Image Analysis

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01-04-2021 | APPLICATION PROBLEMS

Unsafe Construction Behavior Classification Using Deep Convolutional Neural Network

Authors: P. D. Hung, N. T. Su

Published in: Pattern Recognition and Image Analysis | Issue 2/2021

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Abstract

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%.

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Title: Unsafe Construction Behavior Classification Using Deep Convolutional Neural Network
Authors: P. D. Hung
N. T. Su
Publication date: 01-04-2021
Publisher: Pleiades Publishing
Published in: Pattern Recognition and Image Analysis / Issue 2/2021
Print ISSN: 1054-6618
Electronic ISSN: 1555-6212
DOI: https://doi.org/10.1134/S1054661821020073

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