Study of a QueryPNet Model for Accurate Detection and Segmentation of Goose Body Edge Contours
With the rapid development of computer vision, the application of computer vision to precision farming in animal husbandry is currently a hot research topic. Due to the scale of goose breeding continuing to expand, there are higher requirements for the efficiency of goose farming. To achieve precisi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/12/19/2653 |
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| author | Jiao Li Houcheng Su Xingze Zheng Yixin Liu Ruoran Zhou Linghui Xu Qinli Liu Daixian Liu Zhiling Wang Xuliang Duan |
| author_facet | Jiao Li Houcheng Su Xingze Zheng Yixin Liu Ruoran Zhou Linghui Xu Qinli Liu Daixian Liu Zhiling Wang Xuliang Duan |
| author_sort | Jiao Li |
| collection | DOAJ |
| description | With the rapid development of computer vision, the application of computer vision to precision farming in animal husbandry is currently a hot research topic. Due to the scale of goose breeding continuing to expand, there are higher requirements for the efficiency of goose farming. To achieve precision animal husbandry and to avoid human influence on breeding, real-time automated monitoring methods have been used in this area. To be specific, on the basis of instance segmentation, the activities of individual geese are accurately detected, counted, and analyzed, which is effective for achieving traceability of the condition of the flock and reducing breeding costs. We trained QueryPNet, an advanced model, which could effectively perform segmentation and extraction of geese flock. Meanwhile, we proposed a novel neck module that improved the feature pyramid structure, making feature fusion more effective for both target detection and instance individual segmentation. At the same time, the number of model parameters was reduced by a rational design. This solution was tested on 639 datasets collected and labeled on specially created free-range goose farms. With the occlusion of vegetation and litters, the accuracies of the target detection and instance segmentation reached 0.963 (mAP@0.5) and 0.963 (mAP@0.5), respectively. |
| first_indexed | 2024-03-09T22:08:54Z |
| format | Article |
| id | doaj.art-e2ecd56920c348c0957d1dc9fa72fcc7 |
| institution | Directory Open Access Journal |
| issn | 2076-2615 |
| language | English |
| last_indexed | 2024-03-09T22:08:54Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj.art-e2ecd56920c348c0957d1dc9fa72fcc72023-11-23T19:37:29ZengMDPI AGAnimals2076-26152022-10-011219265310.3390/ani12192653Study of a QueryPNet Model for Accurate Detection and Segmentation of Goose Body Edge ContoursJiao Li0Houcheng Su1Xingze Zheng2Yixin Liu3Ruoran Zhou4Linghui Xu5Qinli Liu6Daixian Liu7Zhiling Wang8Xuliang Duan9College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaInstitute of Collaborative Innovation, University of Macau, Taipa, Macau 999077, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaCollege of Information Engineering, Sichuan Agricultural University, Ya’an 625000, ChinaWith the rapid development of computer vision, the application of computer vision to precision farming in animal husbandry is currently a hot research topic. Due to the scale of goose breeding continuing to expand, there are higher requirements for the efficiency of goose farming. To achieve precision animal husbandry and to avoid human influence on breeding, real-time automated monitoring methods have been used in this area. To be specific, on the basis of instance segmentation, the activities of individual geese are accurately detected, counted, and analyzed, which is effective for achieving traceability of the condition of the flock and reducing breeding costs. We trained QueryPNet, an advanced model, which could effectively perform segmentation and extraction of geese flock. Meanwhile, we proposed a novel neck module that improved the feature pyramid structure, making feature fusion more effective for both target detection and instance individual segmentation. At the same time, the number of model parameters was reduced by a rational design. This solution was tested on 639 datasets collected and labeled on specially created free-range goose farms. With the occlusion of vegetation and litters, the accuracies of the target detection and instance segmentation reached 0.963 (mAP@0.5) and 0.963 (mAP@0.5), respectively.https://www.mdpi.com/2076-2615/12/19/2653precision animal husbandrycomputer visioninstance segmentationtarget detectionneck module |
| spellingShingle | Jiao Li Houcheng Su Xingze Zheng Yixin Liu Ruoran Zhou Linghui Xu Qinli Liu Daixian Liu Zhiling Wang Xuliang Duan Study of a QueryPNet Model for Accurate Detection and Segmentation of Goose Body Edge Contours Animals precision animal husbandry computer vision instance segmentation target detection neck module |
| title | Study of a QueryPNet Model for Accurate Detection and Segmentation of Goose Body Edge Contours |
| title_full | Study of a QueryPNet Model for Accurate Detection and Segmentation of Goose Body Edge Contours |
| title_fullStr | Study of a QueryPNet Model for Accurate Detection and Segmentation of Goose Body Edge Contours |
| title_full_unstemmed | Study of a QueryPNet Model for Accurate Detection and Segmentation of Goose Body Edge Contours |
| title_short | Study of a QueryPNet Model for Accurate Detection and Segmentation of Goose Body Edge Contours |
| title_sort | study of a querypnet model for accurate detection and segmentation of goose body edge contours |
| topic | precision animal husbandry computer vision instance segmentation target detection neck module |
| url | https://www.mdpi.com/2076-2615/12/19/2653 |
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