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Smart home device detection is a critical aspect of human-computerinteraction. However, detecting targets in indoor environments can bechallenging due to interference from ambient light and background noise. Inthis paper, we present a new model called FSA-YOLOv5, which addresses thelimitations of traditional convolutional neural networks by introducing theTransformer to learn long-range dependencies. Additionally, we propose a newattention module, the full-separation attention module, which integratesspatial and channel dimensional information to learn contextual information. Toimprove tiny device detection, we include a prediction head for the indoorsmart home device detection task. We also release the Southeast UniversityIndoor Smart Speaker Dataset (SUSSD) to supplement existing data samples.Through a series of experiments on SUSSD, we demonstrate that our methodoutperforms other methods, highlighting the effectiveness of FSA-YOLOv5.

Smart Home Device Detection Algorithm Based on FSA-YOLOv5