Abstract
In constantly changing natural habitats, the conservation of wildlife and their monitoring pose serious issues that reflect over the ecological study as well as in preservation of biodiversity. For prompt interventions, well-informed choices of policy and also automated ecological surveys, accurate categorization of all species along with proper detection are very crucial. This research makes use of visual data in several different environmental settings that provides a viable deep learning system for animal species identification and also their categorization. To achieve accurate localization and species-level classification, two advanced deep learning models are combined in order to handle both spatial localization within the given image or frame and also for accurate classification: (i) a Custom EfficientNet-B4 Convolutional Neural Network (CNN) optimized for species-level classification and (ii) YOLOv10 for detection of the animal’s position with accurate bounding box localization The models make use of an extensive collection of engineering characteristics that includes contextual signals from the surrounding environment, color and texture data along with multi-scale picture representations. For improving resilience to changes in illumination, occlusion as well as background clutter, techniques such as data augmentation and normalization are adapted and used. Varied types of applications including automated camera trap analysis, monitoring of ecology and also animal tracking is made possible by the proposed framework’s that emphasises on modularity, interpretability as well as deployment readiness factors. This study provides an intelligent tool that is also scalable for helping conservation efforts and for the evaluation of biodiversity by making use of a combined feature-rich categorization along with precise detection system.
Keywords: Custom CNN, Deep Learning, EfficientNet-B4, Object Detection, Wildlife Classification, YOLOv10.