Smart home device detection is a critical aspect of human-computer interaction. However, detecting targets in indoor environments can be challenging due to interference from ambient light and background noise. In this paper, we present a new model called FSA-YOLOv5, which addresses the limitations of traditional convolutional neural networks by introducing the Transformer to learn long-range dependencies. Additionally, we propose a new attention module, the full-separation attention module, which integrates spatial and channel dimensional information to learn contextual information. To improve tiny device detection, we include a prediction head for the indoor smart home device detection task. We also release the Southeast University Indoor Smart Speaker Dataset (SUSSD) to supplement existing data samples. Through a series of experiments on SUSSD, we demonstrate that our method outperforms other methods, highlighting the effectiveness of FSA-YOLOv5.
@article{arxiv.2305.04534,
title = {Smart Home Device Detection Algorithm Based on FSA-YOLOv5},
author = {Jiafeng Zhang and Xuejing Pu},
journal= {arXiv preprint arXiv:2305.04534},
year = {2023}
}