Design of an intelligent classification system for domestic garbage based on improved YOLOv5 algorithm

YE Hanyu; GUO Laide; LI Yuexian; DENG Wenbo

doi:10.13205/j.hjgc.202504023

Volume 43 Issue 4

Apr. 2025

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2025 > 43(4): 232-241

YE H Y,GUO L D,LI Y X,et al.Design of an intelligent classification system for domestic garbage based on improved YOLOv5 algorithm[J].Environmental Engineering,2025,43(4):232-241. doi: 10.13205/j.hjgc.202504023

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Design of an intelligent classification system for domestic garbage based on improved YOLOv5 algorithm

doi: 10.13205/j.hjgc.202504023

1. School of Innovation and Entrepreneurship, Liaoning Petrochemical University, Fushun 113001, China;
2. School of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun 113001, China;
3. School of Information and Control Engineering, Liaoning Petrochemical University, Fushun 113001, China

Received Date: 2024-04-18
Accepted Date: 2024-06-14
Rev Recd Date: 2024-06-04
Publish Date: 2025-04-01

Abstract

Abstract

This research proposes a design strategy for an intelligent classification system for domestic garbage based on the improved YOLOv5 algorithm. The aim is to enhance both real-time and accuracy in garbage category identification during the domestic garbage sorting process， addressing the issues of difficult domestic garbage classification， low resource recycling rate， and serious environmental pollution. The algorithm uses YOLOv5 as the base network， replaces the C3 module in the original YOLOv5 architecture with the C3Ghost module of GhostNet， employs an updated loss function SIoU， and utilizes the cascade structure of the Ghost convolution combined with the CBAM attention mechanism to achieve the dual goals of lightweighting the backbone network and improving the model performance. The experimental results demonstrated that the algorithm reduced the weight of the network from 7.111 M to 4.039 M compared with the YOLOv5 base network. Additionally， it improved the frame rate from 73.8 FPS to 82.0 FPS， lightened the model by 50%， enhanced portability to mobile devices， and exhibitsed robust performance and detection capabilities. The system employs a single box with multiple points of interaction， integrating technologies such as infrared sensing， laser distance measurement， image recognition， motor drive and others to develop an intelligent classification dustbin featuring automated opening and closing， bag-breaking， sorting， separation of oil and water residue， and overflow feedback capabilities. The bin is capable of effectively handling a wide range of domestic garbage materials. It can effectively process all types of domestic garbage while preventing contact with potential pathogens and enabling intelligent garbage classification. This system not only reduces labor costs but also facilitates material recycling， thereby protecting the human ecological environment.
- YOLOv5,
- lightweight,
- attention mechanism,
- domestic garbage,
- intelligent classification

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References(23)

References

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