  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.
