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| Abstract: |
| Infrared imaging has proven to be a powerful tool in various fields, with particular significance in wildlife monitoring. In this paper, we present an extension of our previous research, focusing on advancing the segmentation of animal regions in enhanced infrared images and expanding the scope to include species identification. Our proposed methodology builds upon the success of the R-CNN (Region-based Convolutional Neural Network) object detection to improve the accuracy and robustness of animal region segmentation, while simultaneously extending our model’s capabilities to identify and classify the species within those regions. By fine-tuning the R-CNN model on a larger dataset that includes annotated infrared images and species labels, we enhance its capacity to not only accurately segment animal regions but also classify them into specific species categories. To assess the performance of our extended model, we employ a comprehensive set of evaluation metrics, including pixel-based metrics like Intersection over Union (IoU), as well as species classification accuracy. Our results demonstrate significant improvements in both region segmentation accuracy and species identification compared to our previous work and existing methods. This research showcases the potential of deep learning techniques, combined with transfer learning, to advance wildlife monitoring applications using infrared imaging. |
| Key words: deep learning R-CNN transfer learning image analysis habitat management |
| DOI：10.11916/j.issn.1005-9113.2024007 |
| Clc Number:TP391.41, TP18 |
| Fund: |
