Meta-YOLOv8: multi-scale few-shot object detection for Chinese medicinal decoction pieces
Abstract Recently, deep learning has been widely used in the field of Chinese Medicinal Decoction Pieces (CMDP) detection to address the limitations of inefficiency and subjectivity of professionals. However, deep learning networks typically require a large amount of data for training, and it is dif...
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| Format: | Article |
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Elsevier
2025-08-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | https://doi.org/10.1007/s44443-025-00232-x |
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| author | Kai Hu Chu-he Lin Xing Jin Hangjuan Lin |
| author_facet | Kai Hu Chu-he Lin Xing Jin Hangjuan Lin |
| author_sort | Kai Hu |
| collection | DOAJ |
| description | Abstract Recently, deep learning has been widely used in the field of Chinese Medicinal Decoction Pieces (CMDP) detection to address the limitations of inefficiency and subjectivity of professionals. However, deep learning networks typically require a large amount of data for training, and it is difficult to collect a large amount of labeled data for thousands of CMDP. This study aims to tackle the challenges posed by limited data availability in the context of CMDP detection. We propose Meta-YOLOv8, a novel few-shot object detection network based on YOLOv8. To effectively integrate YOLOv8 with meta-learning, we introduce three key modules: (i) Multi-Scale Class Feature Extraction Module (CFEM), (ii) Heterogeneous Graph Convolutional Networks (HGCN), and (iii) Multi-Scale Classification Auxiliary Module (CAM). The CFEM extracts high-quality class features at each scale through Task Alignment Learning. The HGCN facilitates context information enhancement and cross-sample information sharing via intra-scale subgraphs and learns intrinsic connections across scales using inter-scale subgraphs to create scale-specific class prototypes. Finally, the CAM leverages these scale-specific class prototypes to support multi-scale classification. We tested the proposed Meta-YOLOv8 model on two common image datasets, VOC and COCO, as well as a self-built CMDP image dataset. Compared to other few-shot object detection approaches, our proposed model achieves competitive results. Compared to YOLOv8, our method improves the mean average precision ( $$mAP_{0.5}$$ m A P 0.5 ) by 2.2% to 20.4% in different settings, with only a marginal increase in the inference time of 6.6%. The proposed approach integrates meta-learning with YOLOv8, enabling accurate identification of novel CMDPs with only a small number of samples. This capability significantly enhances the efficiency and feasibility of CMDP detection, thereby providing strong technical support for the smart development of the Traditional Chinese Medicine (TCM) industry. |
| format | Article |
| id | doaj-art-308f98d01ce149a3a06112e0f277bfb0 |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-308f98d01ce149a3a06112e0f277bfb02025-08-24T11:53:39ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-08-0137711910.1007/s44443-025-00232-xMeta-YOLOv8: multi-scale few-shot object detection for Chinese medicinal decoction piecesKai Hu0Chu-he Lin1Xing Jin2Hangjuan Lin3Department of Pharmacy, Ningbo Municipal Hospital of Traditional Chinese Medicine (TCM), Affiliated Hospital of Zhejiang Chinese Medical UniversityFaculty of Electrical Engineering and Computer Science, Ningbo UniversityFaculty of Electrical Engineering and Computer Science, Ningbo UniversityDepartment of Pharmacy, Ningbo Municipal Hospital of Traditional Chinese Medicine (TCM), Affiliated Hospital of Zhejiang Chinese Medical UniversityAbstract Recently, deep learning has been widely used in the field of Chinese Medicinal Decoction Pieces (CMDP) detection to address the limitations of inefficiency and subjectivity of professionals. However, deep learning networks typically require a large amount of data for training, and it is difficult to collect a large amount of labeled data for thousands of CMDP. This study aims to tackle the challenges posed by limited data availability in the context of CMDP detection. We propose Meta-YOLOv8, a novel few-shot object detection network based on YOLOv8. To effectively integrate YOLOv8 with meta-learning, we introduce three key modules: (i) Multi-Scale Class Feature Extraction Module (CFEM), (ii) Heterogeneous Graph Convolutional Networks (HGCN), and (iii) Multi-Scale Classification Auxiliary Module (CAM). The CFEM extracts high-quality class features at each scale through Task Alignment Learning. The HGCN facilitates context information enhancement and cross-sample information sharing via intra-scale subgraphs and learns intrinsic connections across scales using inter-scale subgraphs to create scale-specific class prototypes. Finally, the CAM leverages these scale-specific class prototypes to support multi-scale classification. We tested the proposed Meta-YOLOv8 model on two common image datasets, VOC and COCO, as well as a self-built CMDP image dataset. Compared to other few-shot object detection approaches, our proposed model achieves competitive results. Compared to YOLOv8, our method improves the mean average precision ( $$mAP_{0.5}$$ m A P 0.5 ) by 2.2% to 20.4% in different settings, with only a marginal increase in the inference time of 6.6%. The proposed approach integrates meta-learning with YOLOv8, enabling accurate identification of novel CMDPs with only a small number of samples. This capability significantly enhances the efficiency and feasibility of CMDP detection, thereby providing strong technical support for the smart development of the Traditional Chinese Medicine (TCM) industry.https://doi.org/10.1007/s44443-025-00232-xFew-shot object detectionMeta-learningYOLOv8Chinese medicinal decoction pieces |
| spellingShingle | Kai Hu Chu-he Lin Xing Jin Hangjuan Lin Meta-YOLOv8: multi-scale few-shot object detection for Chinese medicinal decoction pieces Journal of King Saud University: Computer and Information Sciences Few-shot object detection Meta-learning YOLOv8 Chinese medicinal decoction pieces |
| title | Meta-YOLOv8: multi-scale few-shot object detection for Chinese medicinal decoction pieces |
| title_full | Meta-YOLOv8: multi-scale few-shot object detection for Chinese medicinal decoction pieces |
| title_fullStr | Meta-YOLOv8: multi-scale few-shot object detection for Chinese medicinal decoction pieces |
| title_full_unstemmed | Meta-YOLOv8: multi-scale few-shot object detection for Chinese medicinal decoction pieces |
| title_short | Meta-YOLOv8: multi-scale few-shot object detection for Chinese medicinal decoction pieces |
| title_sort | meta yolov8 multi scale few shot object detection for chinese medicinal decoction pieces |
| topic | Few-shot object detection Meta-learning YOLOv8 Chinese medicinal decoction pieces |
| url | https://doi.org/10.1007/s44443-025-00232-x |
| work_keys_str_mv | AT kaihu metayolov8multiscalefewshotobjectdetectionforchinesemedicinaldecoctionpieces AT chuhelin metayolov8multiscalefewshotobjectdetectionforchinesemedicinaldecoctionpieces AT xingjin metayolov8multiscalefewshotobjectdetectionforchinesemedicinaldecoctionpieces AT hangjuanlin metayolov8multiscalefewshotobjectdetectionforchinesemedicinaldecoctionpieces |