Research on Lightweight Technology for YOLOv10 Model in Cigarette Packaging Recognition for Display

JIANG Siming; TAN Shengda; HUANG Jielun; XIONG Ziqing

doi:10.19554/j.cnki.1001-3563.2026.05.022

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Packaging Engineering ›› 2026, Vol. 47 ›› Issue (5) : 200-209. DOI: 10.19554/j.cnki.1001-3563.2026.05.022

Automatic and Intelligent Technology

Research on Lightweight Technology for YOLOv10 Model in Cigarette Packaging Recognition for Display

JIANG Siming^1,*, TAN Shengda², HUANG Jielun², XIONG Ziqing¹

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Abstract

To address the challenges of cigarette pack image recognition in frontline scenarios of the tobacco industry, specifically the difficulty in detecting small targets and high risk of missed detections in densely packed arrangements, the work aims to propose a lightweight recognition solution tailored for edge deployment, aiming to enhance recognition efficiency and practical applicability. Given the characteristics of in-store display scenarios, such as the small size and dense arrangement of cigarette packs, the loss function of the detection model was optimized to strengthen its ability to learn features from small-scale and high-density targets. Furthermore, model quantization techniques were applied to compress the converged training network, yielding a lightweight model with fewer parameters and lower computational overhead. Experimental results showed that, compared with the baseline model using the original loss function, the optimized model achieved an 8.05 percentage point improvement in cigarette pack image recognition accuracy, reaching 88.16%. After quantization, the model size was compressed by approximately 68.83%, and inference latency on embedded devices was reduced by more than 47.82% relative to the full-precision model, thus significantly enhancing runtime efficiency. In conclusion, this study develops an efficient lightweight method for cigarette pack image recognition. The constructed model exhibits robust recognition performance and excellent resource adaptability, enabling effective deployment on computationally constrained terminal devices. This improves the feasibility, practicality, and operational convenience of automated cigarette pack recognition in real-world business scenarios.

Key words

cigarette packaging recognition / model quantization / YOLOv10

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JIANG Siming, TAN Shengda, HUANG Jielun, XIONG Ziqing. Research on Lightweight Technology for YOLOv10 Model in Cigarette Packaging Recognition for Display[J]. Packaging Engineering. 2026, 47(5): 200-209 https://doi.org/10.19554/j.cnki.1001-3563.2026.05.022

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