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Cho Y, Song E, Ji Y, Yang S, Kim T, Park S, Baek D, Yu S. Multi-Cat Monitoring System Based on Concept Drift Adaptive Machine Learning Architecture. SENSORS (BASEL, SWITZERLAND) 2023; 23:8852. [PMID: 37960551 PMCID: PMC10648833 DOI: 10.3390/s23218852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/29/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023]
Abstract
In multi-cat households, monitoring individual cats' various behaviors is essential for diagnosing their health and ensuring their well-being. This study focuses on the defecation and urination activities of cats, and introduces an adaptive cat identification architecture based on deep learning (DL) and machine learning (ML) methods. The architecture comprises an object detector and a classification module, with the primary focus on the design of the classification component. The DL object detection algorithm, YOLOv4, is used for the cat object detector, with the convolutional neural network, EfficientNetV2, serving as the backbone for our feature extractor in identity classification with several ML classifiers. Additionally, to address changes in cat composition and individual cat appearances in multi-cat households, we propose an adaptive concept drift approach involving retraining the classification module. To support our research, we compile a comprehensive cat body dataset comprising 8934 images of 36 cats. After a rigorous evaluation of different combinations of DL models and classifiers, we find that the support vector machine (SVM) classifier yields the best performance, achieving an impressive identification accuracy of 94.53%. This outstanding result underscores the effectiveness of the system in accurately identifying cats.
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Affiliation(s)
- Yonggi Cho
- Research and Development Department, Codevision Inc., Seoul 03722, Republic of Korea; (Y.C.); (E.S.); (Y.J.); (S.Y.)
| | - Eungyeol Song
- Research and Development Department, Codevision Inc., Seoul 03722, Republic of Korea; (Y.C.); (E.S.); (Y.J.); (S.Y.)
| | - Yeongju Ji
- Research and Development Department, Codevision Inc., Seoul 03722, Republic of Korea; (Y.C.); (E.S.); (Y.J.); (S.Y.)
| | - Saetbyeol Yang
- Research and Development Department, Codevision Inc., Seoul 03722, Republic of Korea; (Y.C.); (E.S.); (Y.J.); (S.Y.)
| | - Taehyun Kim
- Development Department, Valiantx Co., Ltd., Bucheon 14553, Republic of Korea; (T.K.); (S.P.); (D.B.)
| | - Susang Park
- Development Department, Valiantx Co., Ltd., Bucheon 14553, Republic of Korea; (T.K.); (S.P.); (D.B.)
| | - Doosan Baek
- Development Department, Valiantx Co., Ltd., Bucheon 14553, Republic of Korea; (T.K.); (S.P.); (D.B.)
| | - Sunjin Yu
- Department of Culture Techno, Changwon National University, Changwon 51140, Republic of Korea
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You K, Ding L, Jiang Y, Wu Z, Zhou C. End-to-end deep learning for reverse driving trajectory of autonomous bulldozer. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109402] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Bergler C, Gebhard A, Towers JR, Butyrev L, Sutton GJ, Shaw TJH, Maier A, Nöth E. FIN-PRINT a fully-automated multi-stage deep-learning-based framework for the individual recognition of killer whales. Sci Rep 2021; 11:23480. [PMID: 34873193 PMCID: PMC8648837 DOI: 10.1038/s41598-021-02506-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 11/17/2021] [Indexed: 01/10/2023] Open
Abstract
Biometric identification techniques such as photo-identification require an array of unique natural markings to identify individuals. From 1975 to present, Bigg's killer whales have been photo-identified along the west coast of North America, resulting in one of the largest and longest-running cetacean photo-identification datasets. However, data maintenance and analysis are extremely time and resource consuming. This study transfers the procedure of killer whale image identification into a fully automated, multi-stage, deep learning framework, entitled FIN-PRINT. It is composed of multiple sequentially ordered sub-components. FIN-PRINT is trained and evaluated on a dataset collected over an 8-year period (2011-2018) in the coastal waters off western North America, including 121,000 human-annotated identification images of Bigg's killer whales. At first, object detection is performed to identify unique killer whale markings, resulting in 94.4% recall, 94.1% precision, and 93.4% mean-average-precision (mAP). Second, all previously identified natural killer whale markings are extracted. The third step introduces a data enhancement mechanism by filtering between valid and invalid markings from previous processing levels, achieving 92.8% recall, 97.5%, precision, and 95.2% accuracy. The fourth and final step involves multi-class individual recognition. When evaluated on the network test set, it achieved an accuracy of 92.5% with 97.2% top-3 unweighted accuracy (TUA) for the 100 most commonly photo-identified killer whales. Additionally, the method achieved an accuracy of 84.5% and a TUA of 92.9% when applied to the entire 2018 image collection of the 100 most common killer whales. The source code of FIN-PRINT can be adapted to other species and will be publicly available.
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Affiliation(s)
- Christian Bergler
- grid.5330.50000 0001 2107 3311Department of Computer Science - Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Martensstr. 3, 91058 Erlangen, Germany
| | - Alexander Gebhard
- grid.5330.50000 0001 2107 3311Department of Computer Science - Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Martensstr. 3, 91058 Erlangen, Germany
| | - Jared R. Towers
- Bay Cetology, 257 Fir street, Alert Bay, BC V0N 1A0 Canada ,grid.23618.3e0000 0004 0449 2129Pacific Biological Station, Fisheries and Oceans Canada, 3190 Hammond Bay Road, Nanaimo, BC V9T 6N7 Canada
| | - Leonid Butyrev
- grid.5330.50000 0001 2107 3311Department of Computer Science - Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Martensstr. 3, 91058 Erlangen, Germany
| | - Gary J. Sutton
- Bay Cetology, 257 Fir street, Alert Bay, BC V0N 1A0 Canada ,grid.23618.3e0000 0004 0449 2129Pacific Biological Station, Fisheries and Oceans Canada, 3190 Hammond Bay Road, Nanaimo, BC V9T 6N7 Canada
| | - Tasli J. H. Shaw
- Bay Cetology, 257 Fir street, Alert Bay, BC V0N 1A0 Canada ,grid.23618.3e0000 0004 0449 2129Pacific Biological Station, Fisheries and Oceans Canada, 3190 Hammond Bay Road, Nanaimo, BC V9T 6N7 Canada
| | - Andreas Maier
- grid.5330.50000 0001 2107 3311Department of Computer Science - Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Martensstr. 3, 91058 Erlangen, Germany
| | - Elmar Nöth
- grid.5330.50000 0001 2107 3311Department of Computer Science - Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Martensstr. 3, 91058 Erlangen, Germany
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