Smart Home Device Detection Algorithm Based on FSA-YOLOv5

TL;DR

This paper introduces FSA-YOLOv5, a novel deep learning model combining Transformer and attention mechanisms to improve indoor smart home device detection, especially for tiny devices, using a new dataset and outperforming existing methods.

Contribution

The paper presents FSA-YOLOv5, integrating Transformer and a new attention module for enhanced detection of smart home devices, including tiny objects, with a new dataset for evaluation.

Findings

FSA-YOLOv5 outperforms existing detection methods on SUSSD.

The model effectively detects tiny smart home devices.

The proposed attention module improves contextual feature learning.

Abstract

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.