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Real-Time Domestic Garbage Detection Method Based on Improved YOLOv5

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Advances in Artificial Intelligence and Security (ICAIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1586))

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Abstract

With the substantial improvement of people’s living standards, the amount of domestic garbage is increasing rapidly, and intelligent waste classification has become an urgent need in modern society. In this paper, we propose a real-time garbage detection model based on the improved YOLOv5 (you only look once version 5) algorithm. Firstly, mosaic data enhancement is introduced to enrich the background of the detection object and improve the robustness of the network. Secondly, Distance-IOU Non-Maximum Suppression is used to replace the traditional Non-Maximum Suppression to improve the suppression effect of prediction boxes. Finally, the network is further optimized from the aspect of activation function. The experimental results show that among the four versions of YOLOv5, their mean average precision(mAP) all reach more than 84%, The improved YOLOv5x has the best recognition effect, whose mAP reaches 89.4%, which is 2.1% higher than that of YOLOv5x and 5.3% higher than that of YOLOv5s.

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Acknowledgement

This work was supported by “Automatic Garbage Classification System Based on DenseNet” in National training program of innovation and entrepreneurship for undergraduates.

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Authors and Affiliations

  1. School of Computer and Software, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Shengqi Kan, Wei Fang & Jiayi Wu

  2. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China

    Wei Fang

  3. Department of Computer, Texas Tech University, Lubbock, TX, 79409, USA

    Victor S. Sheng

Authors
  1. Shengqi Kan
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  2. Wei Fang
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  3. Jiayi Wu
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  4. Victor S. Sheng
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Corresponding author

Correspondence to Shengqi Kan .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Kan, S., Fang, W., Wu, J., Sheng, V.S. (2022). Real-Time Domestic Garbage Detection Method Based on Improved YOLOv5. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2022. Communications in Computer and Information Science, vol 1586. Springer, Cham. https://doi.org/10.1007/978-3-031-06767-9_5

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  • DOI: https://doi.org/10.1007/978-3-031-06767-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06766-2

  • Online ISBN: 978-3-031-06767-9

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