Federated Object Detection for Quality Inspection in Shared Production

TL;DR

This paper introduces a federated learning approach using YOLOv5 and FedAvg for object detection in manufacturing quality inspection, achieving improved generalization and bounding box accuracy across decentralized, privacy-sensitive datasets.

Contribution

It presents a novel federated object detection method tailored for quality inspection in manufacturing, combining YOLOv5 with FedAvg to handle non-IID data across multiple factories.

Findings

Enhanced model generalization on decentralized datasets

Improved bounding box accuracy over local models

Demonstrated feasibility of FL in manufacturing quality inspection

Abstract

Federated learning (FL) has emerged as a promising approach for training machine learning models on decentralized data without compromising data privacy. In this paper, we propose a FL algorithm for object detection in quality inspection tasks using YOLOv5 as the object detection algorithm and Federated Averaging (FedAvg) as the FL algorithm. We apply this approach to a manufacturing use-case where multiple factories/clients contribute data for training a global object detection model while preserving data privacy on a non-IID dataset. Our experiments demonstrate that our FL approach achieves better generalization performance on the overall clients' test dataset and generates improved bounding boxes around the objects compared to models trained using local clients' datasets. This work showcases the potential of FL for quality inspection tasks in the manufacturing industry and provides valuable insights into the performance and feasibility of utilizing YOLOv5 and FedAvg for federated object detection.