Transfer Learning-Driven Cattle Instance Segmentation Using Deep Learning Models
Among the emerging applications of artificial intelligence is animal instance segmentation, which has provided a practical means for various researchers to accomplish some aim or execute some order. Though video and image processing are two of the several complex tasks in artificial intelligence, th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/14/12/2282 |
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| author | Rotimi-Williams Bello Pius A. Owolawi Etienne A. van Wyk Chunling Tu |
| author_facet | Rotimi-Williams Bello Pius A. Owolawi Etienne A. van Wyk Chunling Tu |
| author_sort | Rotimi-Williams Bello |
| collection | DOAJ |
| description | Among the emerging applications of artificial intelligence is animal instance segmentation, which has provided a practical means for various researchers to accomplish some aim or execute some order. Though video and image processing are two of the several complex tasks in artificial intelligence, these tasks have become more complex due to the large data and resources needed for training deep learning models. However, these challenges are beginning to be overcome by the transfer learning method of deep learning. In furtherance of the application of the transfer learning method, a system is proposed in this study that applies transfer learning to the detection and recognition of animal activity in a typical farm environment using deep learning models. Among the deep learning models compared, Enhanced Mask R-CNN obtained a significant computing time of 0.2 s and 97% mAP results, which are better than the results obtained by Mask R-CNN, Faster R-CNN, SSD, and YOLOv3, respectively. The findings from the results obtained in this study validate the innovative use of transfer learning to address challenges in cattle segmentation by optimizing the segmentation accuracy and processing time (0.2 s) of the proposed Enhanced Mask R-CNN. |
| format | Article |
| id | doaj-art-e89d587fb0e8467f963356b1335929d3 |
| institution | DOAJ |
| issn | 2077-0472 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-e89d587fb0e8467f963356b1335929d32025-08-20T02:57:05ZengMDPI AGAgriculture2077-04722024-12-011412228210.3390/agriculture14122282Transfer Learning-Driven Cattle Instance Segmentation Using Deep Learning ModelsRotimi-Williams Bello0Pius A. Owolawi1Etienne A. van Wyk2Chunling Tu3Department of Computer Systems Engineering, Faculty of Information and Communication Technology, Tshwane University of Technology, Pretoria 0152, South AfricaDepartment of Computer Systems Engineering, Faculty of Information and Communication Technology, Tshwane University of Technology, Pretoria 0152, South AfricaDepartment of Computer Systems Engineering, Faculty of Information and Communication Technology, Tshwane University of Technology, Pretoria 0152, South AfricaDepartment of Computer Systems Engineering, Faculty of Information and Communication Technology, Tshwane University of Technology, Pretoria 0152, South AfricaAmong the emerging applications of artificial intelligence is animal instance segmentation, which has provided a practical means for various researchers to accomplish some aim or execute some order. Though video and image processing are two of the several complex tasks in artificial intelligence, these tasks have become more complex due to the large data and resources needed for training deep learning models. However, these challenges are beginning to be overcome by the transfer learning method of deep learning. In furtherance of the application of the transfer learning method, a system is proposed in this study that applies transfer learning to the detection and recognition of animal activity in a typical farm environment using deep learning models. Among the deep learning models compared, Enhanced Mask R-CNN obtained a significant computing time of 0.2 s and 97% mAP results, which are better than the results obtained by Mask R-CNN, Faster R-CNN, SSD, and YOLOv3, respectively. The findings from the results obtained in this study validate the innovative use of transfer learning to address challenges in cattle segmentation by optimizing the segmentation accuracy and processing time (0.2 s) of the proposed Enhanced Mask R-CNN.https://www.mdpi.com/2077-0472/14/12/2282activity recognitionanimalfarm environmentMask R-CNNtransfer learning |
| spellingShingle | Rotimi-Williams Bello Pius A. Owolawi Etienne A. van Wyk Chunling Tu Transfer Learning-Driven Cattle Instance Segmentation Using Deep Learning Models Agriculture activity recognition animal farm environment Mask R-CNN transfer learning |
| title | Transfer Learning-Driven Cattle Instance Segmentation Using Deep Learning Models |
| title_full | Transfer Learning-Driven Cattle Instance Segmentation Using Deep Learning Models |
| title_fullStr | Transfer Learning-Driven Cattle Instance Segmentation Using Deep Learning Models |
| title_full_unstemmed | Transfer Learning-Driven Cattle Instance Segmentation Using Deep Learning Models |
| title_short | Transfer Learning-Driven Cattle Instance Segmentation Using Deep Learning Models |
| title_sort | transfer learning driven cattle instance segmentation using deep learning models |
| topic | activity recognition animal farm environment Mask R-CNN transfer learning |
| url | https://www.mdpi.com/2077-0472/14/12/2282 |
| work_keys_str_mv | AT rotimiwilliamsbello transferlearningdrivencattleinstancesegmentationusingdeeplearningmodels AT piusaowolawi transferlearningdrivencattleinstancesegmentationusingdeeplearningmodels AT etienneavanwyk transferlearningdrivencattleinstancesegmentationusingdeeplearningmodels AT chunlingtu transferlearningdrivencattleinstancesegmentationusingdeeplearningmodels |