Computer Vision-Based Behavior Analysis for Workplace Efficiency: Bakery Environment Application

Nowadays, increasing efficiency in the service sector by objectively monitoring and tracking business workflows has become a critical requirement for the sustainability of businesses. Traditional monitoring methods are insufficient for providing sustainable performance analysis due to their time-consuming nature and reliance on subjective human judgment. The aim of this study is to develop a system that automatically detects and classifies employee behavior using computer vision and deep learning techniques. As part of the study, data was collected using a camera placed in a real bakery environment. Five basic classes were labeled on the created data set: cleaning, product interaction, computer use, phone use, and money interaction. The current YOLOv11 (You Only Look Once) architecture, which offers high speed and accuracy for object detection and classification, was used. According to the experimental results obtained from training the model, the system demonstrated high performance, achieving 0.9552 Precision, 0.9324 Recall, 0.9437 F1-Score, and 0.9644 mAP@50 values. These results demonstrate that the proposed system can detect employee behaviors in real-time with a high accuracy rate, allowing it to be used as an effective tool in workplace productivity enhancement and performance evaluation processes.