DEVELOPMENT OF A DEEP LEARNING-POWERED SYSTEM FOR CLASSIFYING CONSTRUCTION WORKER PRODUCTIVITY PATTERNS Kobayashi Tanaka

Аннотация

In the construction industry, worker productivity is crucial for project success, but it is often challenging to accurately assess and monitor. Traditional manual methods of evaluating productivity can be time-consuming and subjective. This study presents the development of an automated system for classifying the productivity behavior of construction workers using deep learning. By employing computer vision and deep learning techniques, the system captures real-time video footage of workers and classifies their behavior into different productivity categories. The system is trained using a large dataset of labeled images representing various worker actions. The results indicate that the system is capable of accurately classifying worker behavior with high precision, offering a more efficient and objective way of assessing productivity on construction sites. Keywords: Automated System, Deep Learning, Productivity Behavior, Construction Workers, Convolutional Neural Network, Video Analysis, Real-time Monitoring, Worker Classification, Construction Industry.