An Accurate Book Spine Detection Network Based on Improved Oriented R-CNN
Book localization is crucial for the development of intelligent book inventory systems, where the high-precision detection of book spines is a critical requirement. However, the varying tilt angles and diverse aspect ratios of books on library shelves often reduce the effectiveness of conventional o...
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/24/7996 |
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| author | Haibo Ma Chaobo Wang Ang Li Aide Xu Dong Han |
| author_facet | Haibo Ma Chaobo Wang Ang Li Aide Xu Dong Han |
| author_sort | Haibo Ma |
| collection | DOAJ |
| description | Book localization is crucial for the development of intelligent book inventory systems, where the high-precision detection of book spines is a critical requirement. However, the varying tilt angles and diverse aspect ratios of books on library shelves often reduce the effectiveness of conventional object detection algorithms. To address these challenges, this study proposes an enhanced oriented R-CNN algorithm for book spine detection. First, we replace the standard 3 × 3 convolutions in ResNet50’s residual blocks with deformable convolutions to enhance the network’s capacity for modeling the geometric deformations of book spines. Additionally, the PAFPN (Path Aggregation Feature Pyramid Network) was integrated into the neck structure to enhance multi-scale feature fusion. To further optimize the anchor box design, we introduce an adaptive initial cluster center selection method for K-median clustering. This allows for a more accurate computation of anchor box aspect ratios that are better aligned with the book spine dataset, enhancing the model’s training performance. We conducted comparison experiments between the proposed model and other state-of-the-art models on the book spine dataset, and the results demonstrate that the proposed approach reaches an mAP of 90.22%, which outperforms the baseline algorithm by 4.47 percentage points. Our method significantly improves detection accuracy, making it highly effective for identifying book spines in real-world library environments. |
| format | Article |
| id | doaj-art-649aedf3a9824908b8bd223d0d3c9f37 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-649aedf3a9824908b8bd223d0d3c9f372025-08-20T02:56:55ZengMDPI AGSensors1424-82202024-12-012424799610.3390/s24247996An Accurate Book Spine Detection Network Based on Improved Oriented R-CNNHaibo Ma0Chaobo Wang1Ang Li2Aide Xu3Dong Han4Library, Panjin Campus of Dalian University of Technology, Panjin 124000, ChinaSchool of Information Science and Technology, Dalian Maritime University, Dalian 116026, ChinaSchool of Information Science and Technology, Dalian Maritime University, Dalian 116026, ChinaSchool of Information Science and Technology, Dalian Maritime University, Dalian 116026, ChinaLibrary, Panjin Campus of Dalian University of Technology, Panjin 124000, ChinaBook localization is crucial for the development of intelligent book inventory systems, where the high-precision detection of book spines is a critical requirement. However, the varying tilt angles and diverse aspect ratios of books on library shelves often reduce the effectiveness of conventional object detection algorithms. To address these challenges, this study proposes an enhanced oriented R-CNN algorithm for book spine detection. First, we replace the standard 3 × 3 convolutions in ResNet50’s residual blocks with deformable convolutions to enhance the network’s capacity for modeling the geometric deformations of book spines. Additionally, the PAFPN (Path Aggregation Feature Pyramid Network) was integrated into the neck structure to enhance multi-scale feature fusion. To further optimize the anchor box design, we introduce an adaptive initial cluster center selection method for K-median clustering. This allows for a more accurate computation of anchor box aspect ratios that are better aligned with the book spine dataset, enhancing the model’s training performance. We conducted comparison experiments between the proposed model and other state-of-the-art models on the book spine dataset, and the results demonstrate that the proposed approach reaches an mAP of 90.22%, which outperforms the baseline algorithm by 4.47 percentage points. Our method significantly improves detection accuracy, making it highly effective for identifying book spines in real-world library environments.https://www.mdpi.com/1424-8220/24/24/7996book spine detectionoriented R-CNNdeformable convolutionsecondary feature fusionK-median clustering |
| spellingShingle | Haibo Ma Chaobo Wang Ang Li Aide Xu Dong Han An Accurate Book Spine Detection Network Based on Improved Oriented R-CNN Sensors book spine detection oriented R-CNN deformable convolution secondary feature fusion K-median clustering |
| title | An Accurate Book Spine Detection Network Based on Improved Oriented R-CNN |
| title_full | An Accurate Book Spine Detection Network Based on Improved Oriented R-CNN |
| title_fullStr | An Accurate Book Spine Detection Network Based on Improved Oriented R-CNN |
| title_full_unstemmed | An Accurate Book Spine Detection Network Based on Improved Oriented R-CNN |
| title_short | An Accurate Book Spine Detection Network Based on Improved Oriented R-CNN |
| title_sort | accurate book spine detection network based on improved oriented r cnn |
| topic | book spine detection oriented R-CNN deformable convolution secondary feature fusion K-median clustering |
| url | https://www.mdpi.com/1424-8220/24/24/7996 |
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