Home appliance integrity detection model based on improved YOLOv4-Tiny

Jianan Liang; Zelong Zhuang; Lei Zhou; Yongjun Cao; Weiwen Chen

doi:10.1117/12.2637173

15 June 2022 Home appliance integrity detection model based on improved YOLOv4-Tiny

Jianan Liang, Zelong Zhuang, Lei Zhou, Yongjun Cao, Weiwen Chen

Proceedings Volume 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022); 1228515 (2022) https://doi.org/10.1117/12.2637173
Event: International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 2022, Zhuhai, China

Abstract

To realize the rapid detection of missing items in home appliances, we proposed a new method based on an improved YOLOv4-Tiny model. Mosaic and Mixup data augmentation methods are used to enrich image data sets, and the SE-Block module is used to apply an attention mechanism on the channels of the feature layer. Experiments show that: The mAP and Recall of the improved YOLOv4-Tiny model proposed in this paper are 97.55% and 95.31%, respectively, which are 3.64% and 5.17% higher than the original YOLOv4-Tiny model, and the FPS reaches 181. The model accuracy is improved without losing detection speed. The proposed method provides technical support for detecting missing items of household appliances.

Citation Download Citation

Jianan Liang, Zelong Zhuang, Lei Zhou, Yongjun Cao, and Weiwen Chen "Home appliance integrity detection model based on improved YOLOv4-Tiny", Proc. SPIE 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 1228515 (15 June 2022); https://doi.org/10.1117/12.2637173

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