Fresh Tea Sprouts Detection via Image Enhancement and Fusion SSD
The accuracy of Fresh Tea Sprouts Detection (FTSD) is not high enough, which has become a big bottleneck in the field of vision-based automatic tea picking technology. In order to improve the detection performance, we rethink the process of FTSD. Meanwhile, motivated by the multispectral image proce...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6614672 |
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| _version_ | 1850169386992664576 |
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| author | Bin Chen Jili Yan Ke Wang |
| author_facet | Bin Chen Jili Yan Ke Wang |
| author_sort | Bin Chen |
| collection | DOAJ |
| description | The accuracy of Fresh Tea Sprouts Detection (FTSD) is not high enough, which has become a big bottleneck in the field of vision-based automatic tea picking technology. In order to improve the detection performance, we rethink the process of FTSD. Meanwhile, motivated by the multispectral image processing, we find that more input information can lead to a better detection result. With this in mind, a novel Fresh Tea Sprouts Detection method via Image Enhancement and Fusion Single-Shot Detector (FTSD-IEFSSD) is proposed in this paper. Firstly, we obtain an enhanced image via RGB-channel-transform-based image enhancement algorithm, which uses the original fresh tea sprouts color image as the input. The enhanced image can provide more input information, where the contrast in the fresh tea sprouts area is increased and the background area is decreased. Then, the enhanced image and color image is used in the detection subnetwork with the backbone of ResNet50 separately. We also use the multilayer semantic fusion and scores fusion to further improve the detection accuracy. The strategy of tea sprouts shape-based default boxes is also included during the training. The experimental results show that the proposed method has a better performance on FTSD than the state-of-the-art methods. |
| format | Article |
| id | doaj-art-a8cd9477c00747ab887c496b3efe3736 |
| institution | OA Journals |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-a8cd9477c00747ab887c496b3efe37362025-08-20T02:20:44ZengWileyJournal of Control Science and Engineering1687-52491687-52572021-01-01202110.1155/2021/66146726614672Fresh Tea Sprouts Detection via Image Enhancement and Fusion SSDBin Chen0Jili Yan1Ke Wang2College of Mathematics Physics and Information Engineering, Jiaxing University, Jiaxing 314000, ChinaCollege of Mathematics Physics and Information Engineering, Jiaxing University, Jiaxing 314000, ChinaCollege of Mathematics Physics and Information Engineering, Jiaxing University, Jiaxing 314000, ChinaThe accuracy of Fresh Tea Sprouts Detection (FTSD) is not high enough, which has become a big bottleneck in the field of vision-based automatic tea picking technology. In order to improve the detection performance, we rethink the process of FTSD. Meanwhile, motivated by the multispectral image processing, we find that more input information can lead to a better detection result. With this in mind, a novel Fresh Tea Sprouts Detection method via Image Enhancement and Fusion Single-Shot Detector (FTSD-IEFSSD) is proposed in this paper. Firstly, we obtain an enhanced image via RGB-channel-transform-based image enhancement algorithm, which uses the original fresh tea sprouts color image as the input. The enhanced image can provide more input information, where the contrast in the fresh tea sprouts area is increased and the background area is decreased. Then, the enhanced image and color image is used in the detection subnetwork with the backbone of ResNet50 separately. We also use the multilayer semantic fusion and scores fusion to further improve the detection accuracy. The strategy of tea sprouts shape-based default boxes is also included during the training. The experimental results show that the proposed method has a better performance on FTSD than the state-of-the-art methods.http://dx.doi.org/10.1155/2021/6614672 |
| spellingShingle | Bin Chen Jili Yan Ke Wang Fresh Tea Sprouts Detection via Image Enhancement and Fusion SSD Journal of Control Science and Engineering |
| title | Fresh Tea Sprouts Detection via Image Enhancement and Fusion SSD |
| title_full | Fresh Tea Sprouts Detection via Image Enhancement and Fusion SSD |
| title_fullStr | Fresh Tea Sprouts Detection via Image Enhancement and Fusion SSD |
| title_full_unstemmed | Fresh Tea Sprouts Detection via Image Enhancement and Fusion SSD |
| title_short | Fresh Tea Sprouts Detection via Image Enhancement and Fusion SSD |
| title_sort | fresh tea sprouts detection via image enhancement and fusion ssd |
| url | http://dx.doi.org/10.1155/2021/6614672 |
| work_keys_str_mv | AT binchen freshteasproutsdetectionviaimageenhancementandfusionssd AT jiliyan freshteasproutsdetectionviaimageenhancementandfusionssd AT kewang freshteasproutsdetectionviaimageenhancementandfusionssd |