CHDPL-Net: a lightweight network for Chinese herbal decoction pieces detection
Abstract To advance the integration of traditional Chinese medicine (TCM) with next-generation information technologies, the intelligent identification of Chinese herbal decoction pieces (CHDP) has become a crucial research direction. However, the performance of current algorithms remains unsatisfac...
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| Format: | Article |
| Language: | English |
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Springer
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/s44443-025-00111-5 |
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| _version_ | 1849330421076066304 |
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| author | Chuhe Lin Zhijun Xie Xing Jin Hangjuan Lin Renguang Shan |
| author_facet | Chuhe Lin Zhijun Xie Xing Jin Hangjuan Lin Renguang Shan |
| author_sort | Chuhe Lin |
| collection | DOAJ |
| description | Abstract To advance the integration of traditional Chinese medicine (TCM) with next-generation information technologies, the intelligent identification of Chinese herbal decoction pieces (CHDP) has become a crucial research direction. However, the performance of current algorithms remains unsatisfactory. To address this, we have constructed a diverse CHDP dataset and proposed a lightweight network for CHDP detection, named CHDPL-Net. Based on YOLOv8, this model introduces a new network scaling factor to reduce redundant channels in deep feature maps and optimizes the Neck and Head structures to better accommodate CHDP detection, which primarily involves medium and large targets. Additionally, a newly designed downsampling module, RDown, replaces conventional downsampling methods to reduce computational overhead, while the adopted upsampling module, DySample, significantly enhances the recovery of detailed features. To further improve lightweight performance, we apply GhostConv to optimize the SPPF and C2F modules and incorporate a novel attention mechanism, EHA, which makes the model more sensitive to color and texture information, mitigating the performance degradation caused by lightweight design. Ultimately, CHDPL-Net achieved excellent results with only 31.9% of the Parameters and 30.6% of the FLOPs compared to YOLOv8, obtaining $$mAP_{50}$$ m A P 50 and $$mAP_{50:95}$$ m A P 50 : 95 scores of 98.2% and 95.4%, respectively, with only a 0.8% performance drop. This demonstrates that the model can meet practical detection needs to a certain extent. |
| format | Article |
| id | doaj-art-efd93dadc8cd401cb6054ca456e45845 |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-efd93dadc8cd401cb6054ca456e458452025-08-20T03:46:54ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-08-0137712310.1007/s44443-025-00111-5CHDPL-Net: a lightweight network for Chinese herbal decoction pieces detectionChuhe Lin0Zhijun Xie1Xing Jin2Hangjuan Lin3Renguang Shan4Faculty of Electrical Engineering and Computer Science, Ningbo UniversityFaculty of Electrical Engineering and Computer Science, Ningbo UniversityFaculty of Electrical Engineering and Computer Science, Ningbo UniversityDepartment of Pharmacy, Ningbo Hospital of Traditional Chinese MedicineDepartment of Pharmacy, Ningbo Hospital of Traditional Chinese MedicineAbstract To advance the integration of traditional Chinese medicine (TCM) with next-generation information technologies, the intelligent identification of Chinese herbal decoction pieces (CHDP) has become a crucial research direction. However, the performance of current algorithms remains unsatisfactory. To address this, we have constructed a diverse CHDP dataset and proposed a lightweight network for CHDP detection, named CHDPL-Net. Based on YOLOv8, this model introduces a new network scaling factor to reduce redundant channels in deep feature maps and optimizes the Neck and Head structures to better accommodate CHDP detection, which primarily involves medium and large targets. Additionally, a newly designed downsampling module, RDown, replaces conventional downsampling methods to reduce computational overhead, while the adopted upsampling module, DySample, significantly enhances the recovery of detailed features. To further improve lightweight performance, we apply GhostConv to optimize the SPPF and C2F modules and incorporate a novel attention mechanism, EHA, which makes the model more sensitive to color and texture information, mitigating the performance degradation caused by lightweight design. Ultimately, CHDPL-Net achieved excellent results with only 31.9% of the Parameters and 30.6% of the FLOPs compared to YOLOv8, obtaining $$mAP_{50}$$ m A P 50 and $$mAP_{50:95}$$ m A P 50 : 95 scores of 98.2% and 95.4%, respectively, with only a 0.8% performance drop. This demonstrates that the model can meet practical detection needs to a certain extent.https://doi.org/10.1007/s44443-025-00111-5Chinese herbal decoction piecesObject detectYOLOv8Lightweight model |
| spellingShingle | Chuhe Lin Zhijun Xie Xing Jin Hangjuan Lin Renguang Shan CHDPL-Net: a lightweight network for Chinese herbal decoction pieces detection Journal of King Saud University: Computer and Information Sciences Chinese herbal decoction pieces Object detect YOLOv8 Lightweight model |
| title | CHDPL-Net: a lightweight network for Chinese herbal decoction pieces detection |
| title_full | CHDPL-Net: a lightweight network for Chinese herbal decoction pieces detection |
| title_fullStr | CHDPL-Net: a lightweight network for Chinese herbal decoction pieces detection |
| title_full_unstemmed | CHDPL-Net: a lightweight network for Chinese herbal decoction pieces detection |
| title_short | CHDPL-Net: a lightweight network for Chinese herbal decoction pieces detection |
| title_sort | chdpl net a lightweight network for chinese herbal decoction pieces detection |
| topic | Chinese herbal decoction pieces Object detect YOLOv8 Lightweight model |
| url | https://doi.org/10.1007/s44443-025-00111-5 |
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