Enhancing Object Detection with Hybrid dataset in Manufacturing Environments: Comparing Federated Learning to Conventional Techniques

Autor:	Hegiste, Vinit, Walunj, Snehal, Antony, Jibinraj, Legler, Tatjana, Ruskowski, Martin
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Federated Learning (FL) has garnered significant attention in manufacturing for its robust model development and privacy-preserving capabilities. This paper contributes to research focused on the robustness of FL models in object detection, hereby presenting a comparative study with conventional techniques using a hybrid dataset for small object detection. Our findings demonstrate the superior performance of FL over centralized training models and different deep learning techniques when tested on test data recorded in a different environment with a variety of object viewpoints, lighting conditions, cluttered backgrounds, etc. These results highlight the potential of FL in achieving robust global models that perform efficiently even in unseen environments. The study provides valuable insights for deploying resilient object detection models in manufacturing environments. Comment: Submitted and Presented at the IEEE International Conference on Innovative Engineering Sciences and Technological Research (ICIESTR-2024)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.08974 Zobrazit plný text záznamu View this record from Arxiv