On Image Ｒecognition Using Bidirectional Feature Pyramid and Deep Neural Network
Object recognition is one of the fundamental tasks in the area of computer vision. The development of deep neural networks advances the object recognition. Nonetheless，multi-scale object recognition still remains to be a challenging task. The feature pyramid is a promising technology to address the...
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| Format: | Article |
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Harbin University of Science and Technology Publications
2021-04-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1940 |
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| author | ZHAO Sheng ZHAO Li |
| author_facet | ZHAO Sheng ZHAO Li |
| author_sort | ZHAO Sheng |
| collection | DOAJ |
| description | Object recognition is one of the fundamental tasks in the area of computer vision. The development of deep neural networks advances the object recognition. Nonetheless，multi-scale object recognition still remains to be a challenging task. The feature pyramid is a promising technology to address the multi-scale object recognition. However，the existing feature pyramid-based object recognition schemes usually employed a top-down pathway， which cannot improve the recognition of large-scale objects. To address this issue，a novel bidirectional enhanced feature pyramid-based object recognition scheme is proposed. The proposed scheme can improve the precisions of both large-scale and small-scale object recognition by enabling the semantic information enhancement from both top to down and down to top. The experiment results showed that the proposed scheme can improve the mean average precision by at least 0. 7% in PASCAL VOC dataset and outperformed all the baselines in MS COCO dataset. These findings verified the effectiveness of the proposed scheme. |
| format | Article |
| id | doaj-art-56677f8d33a140398e7874122022bbe2 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2021-04-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-56677f8d33a140398e7874122022bbe22025-08-20T04:00:27ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832021-04-012602445010.15938/j.jhust.2021.02.006On Image Ｒecognition Using Bidirectional Feature Pyramid and Deep Neural NetworkZHAO Sheng0ZHAO Li1PET /CT Center，Third Affiliated Hospital of Kunming Medical University，Kunming 650118，ChinaBasic Medical School，Kunming Medical University，Kunming 650500，ChinaObject recognition is one of the fundamental tasks in the area of computer vision. The development of deep neural networks advances the object recognition. Nonetheless，multi-scale object recognition still remains to be a challenging task. The feature pyramid is a promising technology to address the multi-scale object recognition. However，the existing feature pyramid-based object recognition schemes usually employed a top-down pathway， which cannot improve the recognition of large-scale objects. To address this issue，a novel bidirectional enhanced feature pyramid-based object recognition scheme is proposed. The proposed scheme can improve the precisions of both large-scale and small-scale object recognition by enabling the semantic information enhancement from both top to down and down to top. The experiment results showed that the proposed scheme can improve the mean average precision by at least 0. 7% in PASCAL VOC dataset and outperformed all the baselines in MS COCO dataset. These findings verified the effectiveness of the proposed scheme. https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1940object recognitionfeature pyramiddeep neural networkcomputer vision |
| spellingShingle | ZHAO Sheng ZHAO Li On Image Ｒecognition Using Bidirectional Feature Pyramid and Deep Neural Network Journal of Harbin University of Science and Technology object recognition feature pyramid deep neural network computer vision |
| title | On Image Ｒecognition Using Bidirectional Feature Pyramid and Deep Neural Network |
| title_full | On Image Ｒecognition Using Bidirectional Feature Pyramid and Deep Neural Network |
| title_fullStr | On Image Ｒecognition Using Bidirectional Feature Pyramid and Deep Neural Network |
| title_full_unstemmed | On Image Ｒecognition Using Bidirectional Feature Pyramid and Deep Neural Network |
| title_short | On Image Ｒecognition Using Bidirectional Feature Pyramid and Deep Neural Network |
| title_sort | on image recognition using bidirectional feature pyramid and deep neural network |
| topic | object recognition feature pyramid deep neural network computer vision |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1940 |
| work_keys_str_mv | AT zhaosheng onimagerecognitionusingbidirectionalfeaturepyramidanddeepneuralnetwork AT zhaoli onimagerecognitionusingbidirectionalfeaturepyramidanddeepneuralnetwork |