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

arXiv: https://arxiv.org/abs/2408.08974 · HTML: https://mlsanity.com/paper/2408.08974/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Threats to Federated Learning: A Survey](https://mlsanity.com/paper/2003.02133/) (2020)
- [FedVision: An Online Visual Object Detection Platform Powered by Federated Learning](https://mlsanity.com/paper/2001.06202/) (2020)
- [Unity Perception: Generate Synthetic Data for Computer Vision](https://mlsanity.com/paper/2107.04259/) (2021)
- [Exploiting Multimodal Synthetic Data for Egocentric Human-Object Interaction Detection in an Industrial Scenario](https://mlsanity.com/paper/2306.12152/) (2023)
- [DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting](https://mlsanity.com/paper/2207.07779/) (2022)
- [Application of federated learning in manufacturing](https://mlsanity.com/paper/2208.04664/) (2022)
- [Turning Privacy-preserving Mechanisms against Federated Learning](https://mlsanity.com/paper/2305.05355/) (2023)
- [Federated Ensemble YOLOv5 -- A Better Generalized Object Detection Algorithm](https://mlsanity.com/paper/2306.17829/) (2023)
- [Federated Learning with Intermediate Representation Regularization](https://mlsanity.com/paper/2210.15827/) (2022)
- [Federated Object Detection for Quality Inspection in Shared Production](https://mlsanity.com/paper/2306.17645/) (2023)
- [Efficient Graph-Friendly COCO Metric Computation for Train-Time Model Evaluation](https://mlsanity.com/paper/2207.12120/) (2022)
- [Enhancing Object Detection Performance for Small Objects through Synthetic Data Generation and Proportional Class-Balancing Technique: A Comparative Study in Industrial Scenarios](https://mlsanity.com/paper/2401.12729/) (2024)

## Built on by (most cited first, 4 of 4)

- [Addressing Heterogeneity in Federated Learning: Challenges and Solutions for a Shared Production Environment](https://mlsanity.com/paper/2408.09556/) (2024)
- [Seamless Integration: Sampling Strategies in Federated Learning Systems](https://mlsanity.com/paper/2408.09545/) (2024)
- [Federated Action Recognition for Smart Worker Assistance Using FastPose](https://mlsanity.com/paper/2508.14113/) (2025)
- [Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturing](https://mlsanity.com/paper/2408.10024/) (2024)

## Abstract (arXiv)

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.