A novel age-group classification method for Irrawaddy dolphins based on dorsal fin shape features |
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| Affiliation: | 1. School of Mathematics and Physics, Anqing Normal University, 246133 Anqing, China;2. Research Center of Aquatic Irganism Conservation and Water Ecosystem Restoration in Anhui Province, Anqing Normal University, 246133 Anqing, China;3. College of Life and Science, Anqing Normal University, 246133 Anqing, China;4. School of Mathematics and Computer, Tongling University. 244061 Tongling, China;5. University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, 246133 Anqing, China;1. Humane Society International, London, United Kingdom;2. University of Bristol, School of Veterinary Sciences, Bristol, United Kingdom;2. Physiological Ecology and Bioenergetics Laboratory, University of Central Florida, Orlando, Florida, USA;1. School of Mathematics and Physics, Anqing Normal University, Anqing 246133, P. R. China;2. Key Laboratory of Modeling, Simulation and Control of Complex Ecosystem in Dabie Mountains of Anhui Higher Eduction Institutes, Anqing Normal University, Anqing 246133, PR China |
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| Abstract: | The Irrawaddy dolphin is an endangered marine mammal species; therefore, there is an urgent need to take protective measures, especially in terms of population breeding and evolution. To address this, it is important to understand the age group structure of populations. Unlike biological individual identification and biological object detection based on pattern classification methods, a new age-group classification (AGC) method was developed to classify Irrawaddy dolphins into three age groups: older, middle-aged, and juvenile. Taking into account the relation between the dorsal fin shape features of Irrawaddy dolphins and their age, the AGC method constructed several dorsal fin geometric morphological features, such as leading edge length and dorsal fin height, using edge extraction and curve fitting of dolphin images. After performing a multicollinearity test on these features, nine effective features were obtained. A model was then trained to classify Irrawaddy dolphins according to their age groups. The experimental results demonstrated that the AGC method has a high classification accuracy of 80.20% for older dolphins. In contrast to individual identification and object detection methods, the proposed AGC method facilitates the analysis of population structure stability and dynamics by classifying Irrawaddy dolphins by age. |
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