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Automated Wildlife Detection in Protected Areas with Camera Traps and Deep Learning
This study explores the adaptation of a neural network architecture with a dual-stream attention mechanism for the task of automatic detection of wild animals in their natural habitat using images captured by camera traps. This issue is particularly relevant in the context of automating the monitoring and population assessment of wild animals, which, in turn, plays a crucial role in biodiversity conservation and the regulation of rare and endangered species. The proposed approach is based on a specialized architecture incorporating attention mechanisms, enabling the model to analyze images more effectively by highlighting key objects while minimizing the influence of background elements. This is particularly important in natural environments, where image quality may vary, and animals may be partially obscured by vegetation or blend into their surroundings. To evaluate the effectiveness of the proposed model, the Wildlife Insights dataset was used, which contains a large number of wildlife images obtained from camera traps in various regions. The experiments conducted in this study demonstrated that the proposed approach achieves outstanding accuracy and reliability compared to existing methods, making it a promising solution for automated wildlife monitoring in natural conditions.