"Leveraging CNN, Mobile Net, and Res Net for Intelligent Wild Animal Activity Detection and Alerts"

ABSTRACT

This project aims to detect the presence of wild animals such as Cheetahs, Foxes, Hyenas, Lions, Tigers, and Wolves using deep learning models, including CNN, MobileNet, and ResNet. The system processes images of these animals and triggers immediate responses to ensure the safety of nearby civilians. Upon detecting an animal, the system activates a siren and sends SMS and email notifications to the nearest officer for swift action. The dataset used for training the models contains images of the aforementioned animals, ensuring effective detection within different environmental contexts. This automated approach is designed to minimize human-wildlife conflict by providing timely alerts, allowing authorities to take proactive measures and alert civilians to potential risks. The combination of advanced deep learning techniques with alert mechanisms creates an efficient and scalable solution for enhancing safety in areas prone to wild animal encounters.

KEYWORDS: Wild animal detection, Cheetah, Fox, Hyena, Lion, Tiger, Wolf, CNN, MobileNet, ResNet, automated alert.