Ensemble Deep Learning Framework for Monitoring of Garment Sewing Employee Added Value

The garment sewing industry is a manufacturing sector dedicated to producing clothing and textile products. This industry encompasses a wide range of activities, including sewing and handling garments. These activities are considered value-added activities, while adjusting pieces, waiting for materials, and equipment maintenance are considered non-value-added activities. Tracking these activities is crucial for the production pipeline's efficiency. Typically, activity recognition involves the utilization of multiple deep learning models. Yet, the main obstacles in training these models revolve around identifying the most appropriate architecture and carefully fine-tuning numerous hyperparameters. This process is time-consuming and prone to errors. One potential solution to address these challenges is to incorporate ensemble learning by combining multiple, less powerful base models into a single, more robust model. Thus, this paper introduces an ensemble approach that integrates various activity recognition models through a combination of voting and meta-learning fusion techniques. Experimental results on baseline activity recognition models, including TwinsSVT, VideoMAE, and Conv3D, exhibit accuracy scores of 92%, 87%, and 70 %, respectively. Furthermore, a comparative analysis of different ensemble techniques is provided. The ensemble approach utilizing meta-learning outperforms the best baseline model, achieving an accuracy of 96% by employing extreme gradient boosting (XGBoost) as a meta-learner. This ensemble approach fuses three baseline models: TwinsSVT, VideoMAE, and Conv3D. By aggregating their predictions using various fusing methods including voting and meta-learners. The result of ensethis approach attains a 3.8 % increase in accuracy compared to solely relying on the highest performing individual model.