Um Mapeamento Sistemático sobre Aprendizado de Máquina para a Classificação de Fotografias de Armadilhas Fotográficas
Resumo
Armadilhas fotográficas atuam como ferramentas valiosas para o biomonitoramento, permitindo a coleta de grandes quantidades de dados ambientais. No entanto, esse grande volume de dados demanda um processamento eficiente para a extração de informações relevantes. Este estudo apresenta um mapeamento sistemático de publicações recentes que exploram modelos de aprendizado de máquina para a automação dessa tarefa. Modelos baseados em redes neurais convolucionais (CNNs) predominam publicações recentes, com a série YOLO se destacando como a mais frequentemente implementada. Observa-se ainda, um subaproveitamento de metadados potencialmente valiosos das capturas.Referências
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Ansari, M. Z., Ahmad, F., Taheri, E. N., Reddy, R. G. J., and Mabood, F. (2024). Animal identity recognition using object detection techniques. Procedia Computer Science, 233:651–659.
Bakana, S. R., Zhang, Y., and Twala, B. (2024). Wildare-yolo: A lightweight and efficient wild animal recognition model. Ecological Informatics, 80:102541.
Bhosale, A., Shinde, P., Firke, Y., Patil, S., Mitake, P., and Shinde, S. (2025). Stray dog detection system using yolov5. Procedia Computer Science, 252:806–813.
Bohnett, E., Holmberg, J., Faryabi, S. P., An, L., Ahmad, B., Rashid, W., and Ostrowski, S. (2023). Comparison of two individual identification algorithms for snow leopards (panthera uncia) after automated detection. Ecological Informatics, 77:102214.
Bothmann, L., Wimmer, L., Charrakh, O., Weber, T., Edelhoff, H., Peters, W., and Menzel, A. (2023). Automated wildlife image classification: an active learning tool for ecological applications. Ecological Informatics, 77:102231.
Boukar, M. M., Mahamat, A. A., Djibrine, O. H., and Abubakar, U. B. (2024). Improving the accuracy of animal species classification in camera trap images using transfer learning. In 2024 International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA), pages 1–6. IEEE.
Buxton, R. T., Lendrum, P. E., Crooks, K. R., and Wittemyer, G. (2018). Pairing camera traps and acoustic recorders to monitor the ecological impact of human disturbance. Global Ecology and Conservation, 16:e00493.
Chalmers, C., Fergus, P., Wich, S., Longmore, S. N., Walsh, N. D., Stephens, P. A., and Nuseibeh, A. (2023). Removing human bottlenecks in bird classification using camera trap images and deep learning. Remote Sensing, 15(10):2638.
Conrady, C. R., Er, , Attwood, C. G., Roberson, L. A., and de Vos, L. (2022). Automated detection and classification of southern african roman seabream using mask r-cnn. Ecological Informatics, 69:101593.
Dave, B., Mori, M., Bathani, A., and Goel, P. (2023). Wild animal detection using yolov8. Procedia Computer Science, 230:100–111.
Glover-Kapfer, P., Soto-Navarro, C. A., and Wearn, O. R. (2019). Camera-trapping version 3.0: current constraints and future priorities for development. Remote Sensing in Ecology and Conservation, 5(3):209–223.
Huang, H. C., Lin, C. H., and Liu, J. (2023). Camera with artificial intelligence of things (aiot) technology for wildlife camera trap system. In 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), pages 0252–0258. IEEE.
Ibraheam, M., Li, K. F., and Gebali, F. (2023). An accurate and fast animal species detection system for embedded devices. IEEE Access, 11:23462–23473.
Jenrette, J., Liu, Z. C., Chimote, P., Hastie, T., Fox, E., and Ferretti, F. (2022). Shark detection and classification with machine learning. Ecological Informatics, 69:101673.
Kitchenham, B. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report 2.3, EBSE Technical Report.
Kumar, R., Bhatti, S., Ruk, S. A., and Pathan, N. (2022). Identification of endangered animal species of pakistan using classical and ensemble approach. In 2022 2nd International Conference on Computing and Machine Intelligence (ICMI), pages 1–5. IEEE.
Leonid, T. T., Kanna, H., VJ, C. C., AS, H., and Lokesh, C. (2023). Human wildlife conflict mitigation using yolo algorithm. In 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), pages 1–7. IEEE.
Leorna, S. and Brinkman, T. (2022). Human vs. machine: Detecting wildlife in camera trap images. Ecological Informatics, 72:101876.
Liu, Y., Che, S., Ai, L., Song, C., Zhang, Z., Zhou, Y., and Xian, C. (2024). Camouflage detection: Optimization-based computer vision for alligator sinensis with low detectability in complex wild environments. Ecological Informatics, 83:102802.
Nazir, S. and Kaleem, M. (2024). Object classification and visualization with edge artificial intelligence for a customized camera trap platform. Ecological Informatics, 79:102453.
Njathi, Y., Wanjiku, L., Mugambi, L., Kabi, J. N., Kiarie, G., and wa Maina, C. (2023). Efficient camera trap image annotation using yolov5. In 2023 IEEE AFRICON, pages 1–6. IEEE.
Oliver, R. Y., Iannarilli, F., Ahumada, J., Fegraus, E., Flores, N., Kays, R., and Jetz, W. (2023). Camera trapping expands the view into global biodiversity and its change. Philosophical Transactions of the Royal Society B, 378(1881):20220232.
Powell, R. A. (2000). Animal home ranges and territories and home range estimators. Research techniques in animal ecology: controversies and consequences, 442:65–110.
Roy, A. M., Bhaduri, J., Kumar, T., and Raj, K. (2023). Wildect-yolo: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection. Ecological Informatics, 75:101919.
Salomón, S., Bringas, S., Duque, R., Montaña, J. L., and González, A. J. (2023). Wildlife species recognition using deep learning. In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 221–227. IEEE.
Simões, F., Bouveyron, C., and Precioso, F. (2023). Deepwild: Wildlife identification, localisation and estimation on camera trap videos using deep learning. Ecological Informatics, 75:102095.
Song, Q., Guan, Y., Guo, X., Guo, X., Chen, Y., Wang, H., and Bao, L. (2024). Benchmarking wild bird detection in complex forest scenes. Ecological Informatics, 80:102466.
Trevathan, J., Tan, W. L., Xing, W., Holzner, D., Kerlin, D., Zhou, J., and Castley, G. (2025). A computer vision enhanced iot system for koala monitoring and recognition. Internet of Things, 29:101474.
Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., and Berger-Wolf, T. (2022). Perspectives in machine learning for wildlife conservation. Nature Communications, 13(1):792.
Ukwuoma, C. C., Qin, Z., Yussif, S. B., Happy, M. N., Nneji, G. U., Urama, G. C., and Agobah, H. (2022). Animal species detection and classification framework based on modified multi-scale attention mechanism and feature pyramid network. Scientific African, 16:e01151.
Wang, X., Wen, S., Niu, N., Wang, G., Long, W., Zou, Y., and Huang, M. (2022). Automatic detection for the world’s rarest primates based on a tropical rainforest environment. Global Ecology and Conservation, 38:e02250.
Yang, Z., Tian, Y., and Zhang, J. (2024). Adaptive image processing embedding to make the ecological tasks of deep learning more robust on camera traps images. Ecological Informatics, 82:102705.
Publicado
24/04/2025
Como Citar
TERASAKA, Diego T.; SOUZA, Patricia C. de.
Um Mapeamento Sistemático sobre Aprendizado de Máquina para a Classificação de Fotografias de Armadilhas Fotográficas. In: ESCOLA REGIONAL DE SISTEMAS DE INFORMAÇÃO DE MATO GROSSO, 1. , 2025, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 132-139.
DOI: https://doi.org/10.5753/ersimt.2025.8900.
