Novel dual convolution adaptive focus neural network for book genre classification
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| Publicado en: | PLoS One vol. 20, no. 11 (Nov 2025), p. e0331011 |
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| Autor principal: | |
| Otros Autores: | , , , , , , |
| Publicado: |
Public Library of Science
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 3269854692 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0331011 |2 doi | |
| 035 | |a 3269854692 | ||
| 045 | 2 | |b d20251101 |b d20251130 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Zeng, Qingtao | |
| 245 | 1 | |a Novel dual convolution adaptive focus neural network for book genre classification | |
| 260 | |b Public Library of Science |c Nov 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Book covers typically contain a wealth of information. With the annual increase in the number of books published, deep learning has been utilised to achieve automatic identification and classification of book covers. This approach overcomes the inefficiency of traditional manual classification operations and enhances the management efficiency of modern book retrieval systems. In the realm of computer vision, the YOLO algorithm has garnered significant attention owing to its excellent performance across various visual tasks. Therefore, this study introduces the CPPDE-YOLO model, a novel dual-convolution adaptive focus neural network that integrates the PConv and PWConv operators, alongside dynamic sampling technology and efficient multi-scale attention. By incorporating specific enhancement features, the original YOLOv8 framework has been optimised to yield superior performance in book cover classification. The aim of this model is to significantly enhance the accuracy of image classification by refining the algorithm. For effective book cover classification, it is imperative to consider complex global feature information to capture intricate features while managing computational costs. To address this, we propose a hybrid model that integrates parallel convolution and point-by-point convolution within the backbone network, integrating it into the DualConv framework to capture complex feature information. Moreover, we integrate the efficient multi-scale attention mechanism into each cross stage partial network fusion residual block in the head section to focus on learning key features for more precise classification. The dynamic sampling method is employed instead of the traditional UPsample method to overcome its inherent limitations. Finally, experimental results on real datasets validate the performance enhancement of our proposed CPPDE-YOLO network structure compared to the original YOLOv8 classification structure, achieving Top_1 Accuracy and Top_5 Accuracy improvement of 1.1% and 1.0%, respectively. This underscores the effectiveness of our proposed algorithm in enhancing book genre classification. | |
| 653 | |a Visual tasks | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Algorithms | ||
| 653 | |a Sampling methods | ||
| 653 | |a Books | ||
| 653 | |a Convolution | ||
| 653 | |a Neural networks | ||
| 653 | |a Attention | ||
| 653 | |a Computer vision | ||
| 653 | |a Classification | ||
| 653 | |a Sampling | ||
| 653 | |a Machine learning | ||
| 653 | |a Efficiency | ||
| 653 | |a Performance enhancement | ||
| 653 | |a Novels | ||
| 653 | |a Effectiveness | ||
| 653 | |a Image classification | ||
| 653 | |a Design | ||
| 653 | |a Information processing | ||
| 653 | |a Object recognition | ||
| 653 | |a Economic | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Zhang, Lixin | |
| 700 | 1 | |a Zhao, Jiefeng | |
| 700 | 1 | |a Xu, Anping | |
| 700 | 1 | |a Qi, Yali | |
| 700 | 1 | |a Yu, Liqin | |
| 700 | 1 | |a Li, Wenjing | |
| 700 | 1 | |a Xia, Haochang | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 11 (Nov 2025), p. e0331011 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3269854692/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3269854692/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3269854692/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |