Improved seabird image classification based on dual transfer learning framework and spatial pyramid pooling |
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| Institution: | 1. Agricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata, India;2. Kerala University of Digital Sciences, Innovation and Technology, Thiruvananthapuram, Kerala, India;3. Department of Statistics, Visva-Bharati, Santiniketan, Birbhum, India;1. Key Laboratory of Ministry of Education for Coastal Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Fujian 361102, China;2. Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Xiamen University, 361102, China;3. Xiamen Key Laboratory of Urban Sea Ecological Conservation and Restoration (USER), Xiamen University, 361102, China;4. Coastal and Ocean Management Institute, Xiamen University, 361102, China;5. School of Energy and Environmental Engineering, University of Science and Technology Beijing, 30 Xueyuan Road, Haidian District, Beijing 100083, China;6. Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, Beijing 100083, China;1. Wildlife Institute of India, Dehradun 248001, India;2. Graphic Era University, Dehradun 248002, India;3. Birla Institute of Technology, Mesra, Ranchi 835215, India;1. School of Resources, Environment and Materials, Guangxi University, 530004 Nanning, China;2. State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, China;1. Department of Geographical Sciences, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China;2. Hubei Key Laboratory of Critical Zone Evolution, China University of Geosciences, Wuhan 430074, China;3. Guizhou Electric Power Design Research Institute, Power Construction Corporation of China, Guiyang 550002, Guizhou, China |
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| Abstract: | Seabird plays an important role in the marine ecosystem and is an indispensable part of the food chain. However, the seabird population has been experiencing a rapid decline due to various factors including climate change, fisheries, and invasive non-native species. To better protect seabirds, the first step is to accurately monitor them. Automatic classification of seabirds would significantly speed up the monitoring process. In this paper, we propose a dual transfer learning framework for improved seabird image classification based on spatial pyramid pooling. Specifically, a dual transfer learning framework is used to capture various patterns to improve the discriminability of the proposed model. Both InceptionV3 and DenseNet201 are used as the backbones, whose outputs are concatenated using a spatial pyramid pooling (SPP) layer. Here, SPP is used to address images of different sizes. Next, two types of pooling, global average-pooling (GAP) and global max-pooling (GMP) are applied to the output of the SPP layer, where the results of GAP and GMP are linearly added up. Our method takes both InceptionV3 and DenseNet201 as feature extractors and is trained offline in an end-to-end style. The proposed dual transfer learning framework-based seabird image classification method reached the accuracy, precision, recall, F1-score of 95.11%, 95.33%, 95.11%, 95.13% on the 10 classes seabird image dataset. |
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| Keywords: | Bird sound classification Convolutional Neural Networks Spatial pyramid pooling Global max/average pooling |
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