University Building Recognition Dataset in Thailand for the mission-oriented IoT sensor system

TL;DR

This paper introduces the CUBR dataset for building recognition in Thailand and demonstrates that WAFL training improves accuracy over self-training in edge device image recognition tasks.

Contribution

The creation of the Chulalongkorn University Building Recognition Dataset (CUBR) tailored for mission-oriented IoT sensor systems in Thailand.

Findings

WAFL with Vision Transformer outperforms self-training in accuracy.

CUBR dataset is publicly available for research.

Training on WAFL scenarios yields better results.

Abstract

Many industrial sectors have been using of machine learning at inference mode on edge devices. Future directions show that training on edge devices is promising due to improvements in semiconductor performance. Wireless Ad Hoc Federated Learning (WAFL) has been proposed as a promising approach for collaborative learning with device-to-device communication among edges. In particular, WAFL with Vision Transformer (WAFL-ViT) has been tested on image recognition tasks with the UTokyo Building Recognition Dataset (UTBR). Since WAFL-ViT is a mission-oriented sensor system, it is essential to construct specific datasets by each mission. In our work, we have developed the Chulalongkorn University Building Recognition Dataset (CUBR), which is specialized for Chulalongkorn University as a case study in Thailand. Additionally, our results also demonstrate that training on WAFL scenarios achieves better accuracy than self-training scenarios. Dataset is available in https://github.com/jo2lxq/wafl/.