Improved YOLOv5-based cigarette butt waste suction sanitation robot

Yang Hong; Weiguo Li; Jiahe Gao; Shuo Yang

doi:10.1117/12.3051854

18 November 2024 Improved YOLOv5-based cigarette butt waste suction sanitation robot

Yang Hong, Weiguo Li, Jiahe Gao, Shuo Yang

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134032L (2024) https://doi.org/10.1117/12.3051854
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

The global quantity of cigarette butt waste is staggering. According to data from the World Health Organization, approximately 4.5 trillion cigarette butts are discarded annually. This cigarette butt waste poses a serious threat to both the environment and human health. The filters in cigarette butts are made of non-biodegradable plastic material, which releases harmful substances such as heavy metals when decomposing in natural environments, leading to pollution of soil, water sources, and air. Therefore, the timely collection and treatment of cigarette butt waste has become a pressing issue. This paper introduces a cigarette butt waste suction robot combined with a real-time detection system based on an improved YOLOv5 algorithm. Experiments have shown that the recognition accuracy can reach 97%. The cigarette butt waste suction sanitation robot can efficiently collect cigarette butt waste, achieving automatic collection and processing of cigarette butt waste, improving cleaning efficiency, and reducing labor costs. This contributes to addressing the environmental and health issues caused by cigarette butt waste.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yang Hong, Weiguo Li, Jiahe Gao, and Shuo Yang "Improved YOLOv5-based cigarette butt waste suction sanitation robot", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134032L (18 November 2024); https://doi.org/10.1117/12.3051854

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KEYWORDS

Imaging systems

Cameras

Detection and tracking algorithms

Visualization

Object detection

Education and training

Calibration

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