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JACIII Vol.30 No.4 pp. 1102-1119
(2026)
doi: 10.20965/jaciii.2026.p1102

Research Paper:

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YOLO-GF: A Lightweight Detector for Character-Level Recognition in Medicine Packaging

Shiyong Geng*1,†, Jintao Chen*2, Kuozhan Wang*3, Hengyi Li*3 ORCID Icon, Xuebin Yue*3 ORCID Icon, and Lin Meng*4 ORCID Icon

*1School of Integrated Circuits, Zhongyuan University of Technology
No.1 Huaihe Road, Longhu Town, Xinzheng, Zhengzhou, Henan 451191, China

†Corresponding author

*2The First Affiliated Hospital of Henan University of CM
No.19 Renmin Road, Jinshui District, Zhengzhou, Henan 450000, China

*3School of Automation and Electrical Engineering, Zhongyuan University of Technology
No.41 Zhongyuan Middle Road, Zhongyuan District, Zhengzhou, Henan 450007, China

*4College of Science and Engineering, Ritsumeikan University
1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan

Received:
August 29, 2025
Accepted:
February 13, 2026
Published:
July 20, 2026
Creative Commons License
Keywords:
medicine package detection, object detection, down sampling, spatial pyramid pooling, attention mechanism
Abstract

The aging global population has significantly increased the demand for long-term care, presenting various challenges in elderly healthcare, especially in medication management. Ensuring timely and accurate medication delivery is essential to preventing medication errors, which could otherwise lead to severe health complications. This paper proposes an innovative solution to automate and enhance medication verification in nursing homes through object detection. The proposed system leverages the YOLO-GF framework, which integrates efficient computational techniques with high detection accuracy to enable real-time medicine package detection. A hybrid down sampling (HDS) module, combining max pooling, average pooling, and 2×2 stride convolution, optimizes feature map processing by reducing computational overhead while preserving key spatial information. Additionally, an enhanced multi-scale pyramid pooling (EMSPP) technique is introduced to improve multi-scale feature aggregation, enhancing the model’s ability to capture object features at various resolutions. Unlike existing detection systems that often suffer from high computational cost or poor generalization, our approach explicitly balances speed and accuracy through lightweight architectural design. Extensive experiments validate the effectiveness of the HDS and EMSPP modules in multi-scale feature learning. The YOLO-GF framework achieves a 100.00% mean average precision (mAP) on the medicine package dataset, with an inference speed of 130.16 frames per second, fully meeting the real-time monitoring requirements. Further evaluation of public datasets, including Dish20 (98.30% mAP) and Barcodes (97.34% mAP), demonstrates that YOLO-GF outperforms existing state-of-the-art models. These results highlight the superior generalization capabilities of the proposed framework. This work establishes a novel and efficient real-time solution for medicine package verification by integrating HDS and multi-scale enhancement into a unified YOLO-based framework, significantly improving accuracy and safety over manual processes in healthcare environments. The project can be found at http://www.ihpc.se.ritsumei.ac.jp/obidataset.html.

YOLO-GF overall framework

YOLO-GF overall framework

Full text
Cite this article as:
S. Geng, J. Chen, K. Wang, H. Li, X. Yue, and L. Meng, “YOLO-GF: A Lightweight Detector for Character-Level Recognition in Medicine Packaging,” J. Adv. Comput. Intell. Intell. Inform., Vol.30 No.4, pp. 1102-1119, 2026.
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