Deconvolution Enhancement Keypoint Network for Efficient Fish Fry Counting
Fish fry counting has been vital in fish farming, but current computer-based methods are not feasible enough to accurately and efficiently calculate large number of fry in a single count due to severe occlusion, dense distribution and the small size of fish fry. To address this problem, we propose t...
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| Format: | Article |
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MDPI AG
2024-05-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/14/10/1490 |
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| author | Ximing Li Zhicai Liang Yitao Zhuang Zhe Wang Huan Zhang Yuefang Gao Yubin Guo |
| author_facet | Ximing Li Zhicai Liang Yitao Zhuang Zhe Wang Huan Zhang Yuefang Gao Yubin Guo |
| author_sort | Ximing Li |
| collection | DOAJ |
| description | Fish fry counting has been vital in fish farming, but current computer-based methods are not feasible enough to accurately and efficiently calculate large number of fry in a single count due to severe occlusion, dense distribution and the small size of fish fry. To address this problem, we propose the deconvolution enhancement keypoint network (DEKNet), a method for fish fry counting that features a single-keypoint approach. This novel approach models the fish fry as a point located in the central part of the fish head, laying the foundation for our innovative counting strategy. To be specific, first, a fish fry feature extractor (FFE) characterized by parallel dual branches is designed for high-resolution representation. Next, two identical deconvolution modules (TDMs) are added to the generation head for a high-quality and high-resolution keypoint heatmap with the same resolution size as the input image, thus facilitating the precise counting of fish fry. Then, the local peak value of the heatmap is obtained as the keypoint of the fish fry, so the number of these keypoints with coordinate information equals the number of fry, and the coordinates of the keypoint can be used to locate the fry. Finally, FishFry-2023, a large-scale fish fry dataset, is constructed to evaluate the effectiveness of the method proposed by us. Experimental results show that an accuracy rate of 98.59% was accomplished in fish fry counting. Furthermore, DEKNet achieved a high degree of accuracy on the Penaeus dataset (98.51%) and an MAE of 13.32 on a public dataset known as Adipocyte Cells. The research outcomes reveal that DEKNet has superior comprehensive performance in counting accuracy, the number of parameters and computational effort. |
| first_indexed | 2025-03-21T22:38:15Z |
| format | Article |
| id | doaj.art-f7e61bbe17804993b51a29c66b99344f |
| institution | Directory Open Access Journal |
| issn | 2076-2615 |
| language | English |
| last_indexed | 2025-03-21T22:38:15Z |
| publishDate | 2024-05-01 |
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| record_format | Article |
| series | Animals |
| spelling | doaj.art-f7e61bbe17804993b51a29c66b99344f2024-05-24T13:06:36ZengMDPI AGAnimals2076-26152024-05-011410149010.3390/ani14101490Deconvolution Enhancement Keypoint Network for Efficient Fish Fry CountingXiming Li0Zhicai Liang1Yitao Zhuang2Zhe Wang3Huan Zhang4Yuefang Gao5Yubin Guo6College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, ChinaCollege of Foreign Studies, South China Agricultural University, Guangzhou 510642, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, ChinaCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, ChinaFish fry counting has been vital in fish farming, but current computer-based methods are not feasible enough to accurately and efficiently calculate large number of fry in a single count due to severe occlusion, dense distribution and the small size of fish fry. To address this problem, we propose the deconvolution enhancement keypoint network (DEKNet), a method for fish fry counting that features a single-keypoint approach. This novel approach models the fish fry as a point located in the central part of the fish head, laying the foundation for our innovative counting strategy. To be specific, first, a fish fry feature extractor (FFE) characterized by parallel dual branches is designed for high-resolution representation. Next, two identical deconvolution modules (TDMs) are added to the generation head for a high-quality and high-resolution keypoint heatmap with the same resolution size as the input image, thus facilitating the precise counting of fish fry. Then, the local peak value of the heatmap is obtained as the keypoint of the fish fry, so the number of these keypoints with coordinate information equals the number of fry, and the coordinates of the keypoint can be used to locate the fry. Finally, FishFry-2023, a large-scale fish fry dataset, is constructed to evaluate the effectiveness of the method proposed by us. Experimental results show that an accuracy rate of 98.59% was accomplished in fish fry counting. Furthermore, DEKNet achieved a high degree of accuracy on the Penaeus dataset (98.51%) and an MAE of 13.32 on a public dataset known as Adipocyte Cells. The research outcomes reveal that DEKNet has superior comprehensive performance in counting accuracy, the number of parameters and computational effort.https://www.mdpi.com/2076-2615/14/10/1490fish fry countingkeypointdeconvolutionheatmap |
| spellingShingle | Ximing Li Zhicai Liang Yitao Zhuang Zhe Wang Huan Zhang Yuefang Gao Yubin Guo Deconvolution Enhancement Keypoint Network for Efficient Fish Fry Counting Animals fish fry counting keypoint deconvolution heatmap |
| title | Deconvolution Enhancement Keypoint Network for Efficient Fish Fry Counting |
| title_full | Deconvolution Enhancement Keypoint Network for Efficient Fish Fry Counting |
| title_fullStr | Deconvolution Enhancement Keypoint Network for Efficient Fish Fry Counting |
| title_full_unstemmed | Deconvolution Enhancement Keypoint Network for Efficient Fish Fry Counting |
| title_short | Deconvolution Enhancement Keypoint Network for Efficient Fish Fry Counting |
| title_sort | deconvolution enhancement keypoint network for efficient fish fry counting |
| topic | fish fry counting keypoint deconvolution heatmap |
| url | https://www.mdpi.com/2076-2615/14/10/1490 |
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