Abstract
Camera trap image analysis, although critical for habitat and species conservation, is often a manual, time-consuming, and expensive task. Thus, automating this process would allow large-scale research on biodiversity hotspots of large conspicuous mammals and bird species. This paper explores the use of deep learning species-level object detection and classification models for this task, using two state-of-the-art architectures, YOLOv5 and Faster R-CNN, for two species: white-lipped peccary and collared peccary. The dataset contains 7,733 images obtained after data augmentation from the Tiputini Biodiversity Station. The models were trained in 70% of the dataset, validated in 20%, and tested in 10% of the available data. The Faster R-CNN model achieved an average mAP (Mean Average Precision) of 0.26 at a 0.5 Intersection Over Union (IoU) threshold and 0.114 at a 0.5 to 0.95 IoU threshold, which is comparable with the original results of Faster R-CNN on the MS COCO dataset. Whereas, YOLOv5 achieved an average mAP of 0.5525 at a 0.5 IoU threshold, while its average mAP at a 0.5 to 0.95 IoU threshold is 0.37997. Therefore, the YOLOv5 model was shown to be more robust, having lower losses and a higher overall mAP value than Faster-RCNN and YOLOv5 trained on the MS COCO dataset. This is one of the first steps towards developing an automated camera trap analysis tool, allowing a large-scale analysis of population and habitat trends to benefit their conservation. The results suggest that hyperparameter fine-tuning would improve our models and allow us to extend this tool to other native species.
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Acknowledgment
The authors express their gratitude to the Tiputini Biodiversity Station for providing the data used in this study, which were collected by all researchers and staff working on the TBS-Camara trap project. The station is affiliated with USFQ. Additionally, the authors extend thanks to the Applied Signal Processing and Machine Learning Research Group at USFQ for supplying the computing infrastructure (NVidia DGX workstation) employed for implementing and executing the developed source code.
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Zurita, MJ. et al. (2024). On the Use of Deep Learning Models for Automatic Animal Classification of Native Species in the Amazon. In: Orjuela-Cañón, A.D., Lopez, J.A., Arias-Londoño, J.D. (eds) Applications of Computational Intelligence. ColCACI 2023. Communications in Computer and Information Science, vol 1865. Springer, Cham. https://doi.org/10.1007/978-3-031-48415-5_7
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