Wild animals

We modified the classifier for wildlife-specific de- tection of four target species: lions, elephants, leopards, and monkeys. This adaptation reduces network parameters and computational overhead while enhancing inference speed and detection accuracy for real-time scenarios [13] [14]. Transfer learning techniques were employed to fine-tune pre-trained weights for improved wildlife detection performance. The optimization proves essential for Raspberry Pi 5 deployment in resource-constrained rural environments. Our dataset contains 3,089 annotated images from wildlife repositories and online databases, representing diverse natural environments including forests, grasslands, and agricultural boundaries. The collection ensures variability in backgrounds, lighting conditions, scales,angles, and animal postures to enhance real-world detection reliability. Negative samples containing empty landscapes, domestic animals, and humans were incorporated to minimize false positives. All images feature precise bounding box anno- tations and class labels for the four target species, providing high-quality training data for robust model development.