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Improving the Accuracy of Automatic Wildlife Detection in Nature Reserves Using Infrared Imaging
In this paper, an improved approach for automatic wildlife detection in natural environments based on the integration of a neural network architecture with a two-stream attention mechanism and a novel preclassification step based on infrared data has been presented. The proposed method addresses one of the key challenges in environmental monitoring: the need for scalable and accurate tools for assessing wildlife populations and supporting biodiversity conservation. The preclassification module analyzes color and thermal patterns in the infrared range to improve detection reliability before the main detection stage. This step made it possible to filter out background noise and select areas that are highly likely to contain living organisms, allowing for more focused and accurate subsequent analysis. The core detection system is built on a neural network architecture with a dual attention mechanism that emphasizes semantically significant areas of camera trap images while minimizing the influence of visually complex natural scenes. This is especially important for field data, which are often characterized by partial occlusions, uneven illumination, and rich backgrounds. Performance has been evaluated using the Wildlife Insights dataset, a large and diverse collection of camera trap images collected across different ecological regions. Based on experimental results, the proposed approach provides higher accuracy and robustness compared to traditional models, especially in visually complex environments. By combining infrared color-based preclassification and a detection pipeline enhanced by attention mechanisms, the proposed method significantly improves the efficiency of automated wildlife monitoring. The obtained results have confirmed the applicability of the system for ecological research, conservation planning, and long-term studies of wild animal populations.