DFCCNet: A Dense Flock of Chickens Counting Network Based on Density Map Regression
With the development of artificial intelligence, automatically and accurately counting chickens has become a reality. However, insufficient lighting, irregular sizes, and dense flocks make this a challenging task. The existing methods cannot perform accurate and stable counting. In this article, a d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/13/23/3729 |
| _version_ | 1797400494282375168 |
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| author | Jinze Lv Jinfeng Wang Chaoda Peng Qiong Huang |
| author_facet | Jinze Lv Jinfeng Wang Chaoda Peng Qiong Huang |
| author_sort | Jinze Lv |
| collection | DOAJ |
| description | With the development of artificial intelligence, automatically and accurately counting chickens has become a reality. However, insufficient lighting, irregular sizes, and dense flocks make this a challenging task. The existing methods cannot perform accurate and stable counting. In this article, a dense flock of chickens counting network (DFCCNet) is proposed based on density map regression, where features from different levels are merged using feature fusion to obtain more information for distinguishing chickens from the background, resulting in more stable counting results. Multi-scaling is used to detect and count chickens at various scales, which can improve the counting accuracy and ensure stable performance for chickens of different sizes. Feature convolution kernels are adopted to convolve feature maps, which can extract more accurate target information, reduce the impact of occlusion, and achieve more reliable and precise results. A dataset of dense flocks of chickens (namely Dense-Chicken) has been collected and constructed, which contains 600 images of 99,916 chickens, with labeled points and boxes. It can be accessed by researchers as benchmark data. The proposed method was compared with some state-of-the-art algorithms, to validate its effectiveness. With its robustness being verified by counting in three kinds of density situations, with the mean absolute error being 4.26, 9.85, and 19.17, respectively, and a speed of 16.15 FPS. DFCCNet provides an automatic and fast approach to counting chickens in a dense farming environment. It can be easily embedded into handheld devices for application in agricultural engineering. |
| first_indexed | 2024-03-09T01:56:21Z |
| format | Article |
| id | doaj.art-fc4a3af05a3d4c91b759ff3a1bf00c5e |
| institution | Directory Open Access Journal |
| issn | 2076-2615 |
| language | English |
| last_indexed | 2024-03-09T01:56:21Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj.art-fc4a3af05a3d4c91b759ff3a1bf00c5e2023-12-08T15:10:51ZengMDPI AGAnimals2076-26152023-12-011323372910.3390/ani13233729DFCCNet: A Dense Flock of Chickens Counting Network Based on Density Map RegressionJinze Lv0Jinfeng Wang1Chaoda Peng2Qiong Huang3College 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, ChinaWith the development of artificial intelligence, automatically and accurately counting chickens has become a reality. However, insufficient lighting, irregular sizes, and dense flocks make this a challenging task. The existing methods cannot perform accurate and stable counting. In this article, a dense flock of chickens counting network (DFCCNet) is proposed based on density map regression, where features from different levels are merged using feature fusion to obtain more information for distinguishing chickens from the background, resulting in more stable counting results. Multi-scaling is used to detect and count chickens at various scales, which can improve the counting accuracy and ensure stable performance for chickens of different sizes. Feature convolution kernels are adopted to convolve feature maps, which can extract more accurate target information, reduce the impact of occlusion, and achieve more reliable and precise results. A dataset of dense flocks of chickens (namely Dense-Chicken) has been collected and constructed, which contains 600 images of 99,916 chickens, with labeled points and boxes. It can be accessed by researchers as benchmark data. The proposed method was compared with some state-of-the-art algorithms, to validate its effectiveness. With its robustness being verified by counting in three kinds of density situations, with the mean absolute error being 4.26, 9.85, and 19.17, respectively, and a speed of 16.15 FPS. DFCCNet provides an automatic and fast approach to counting chickens in a dense farming environment. It can be easily embedded into handheld devices for application in agricultural engineering.https://www.mdpi.com/2076-2615/13/23/3729artificial intelligencechicken countingdensity map regressionfeature fusionmulti-scaling |
| spellingShingle | Jinze Lv Jinfeng Wang Chaoda Peng Qiong Huang DFCCNet: A Dense Flock of Chickens Counting Network Based on Density Map Regression Animals artificial intelligence chicken counting density map regression feature fusion multi-scaling |
| title | DFCCNet: A Dense Flock of Chickens Counting Network Based on Density Map Regression |
| title_full | DFCCNet: A Dense Flock of Chickens Counting Network Based on Density Map Regression |
| title_fullStr | DFCCNet: A Dense Flock of Chickens Counting Network Based on Density Map Regression |
| title_full_unstemmed | DFCCNet: A Dense Flock of Chickens Counting Network Based on Density Map Regression |
| title_short | DFCCNet: A Dense Flock of Chickens Counting Network Based on Density Map Regression |
| title_sort | dfccnet a dense flock of chickens counting network based on density map regression |
| topic | artificial intelligence chicken counting density map regression feature fusion multi-scaling |
| url | https://www.mdpi.com/2076-2615/13/23/3729 |
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