An efficient CNN-based approach for automated animal intrusion detection
Ramamani Tripathy, S. V. Achuta Rao, P. Maheswari, Rao K. Mallikharjuna, Balan Santhosh Kumar, Balajee Maram · 2025
This paper focuses on the automated animal entry detection as an important factor in wildlife conservation, agricultural practices, and security, for which CNNs are used. Based on the idea of deep learning and obraz processing, the suggested approach can successfully recognize and sort animals in the captured photo or video stream to save the ecosystems and protect people’s concerns. The structural design involves a CNN model, which is well designed and fine tuned for the purpose of identifying the presence of animals. The method also includes complex processing of the picture, which enhances the model’s stability when handling a range of illumination conditions and backdrop issues. It employs a buena calidad dataset for training and evaluation. It is evident that the CNN model assures an appropriate recognition of the animal presence in a given frame including multiple animals due to the training carried out in the model to detect many types of animals. This type of research reduces the likelihood of animals intruding and using electricity, water, etc. by letting computer systems observe them and alert about potential danger without human input. The CNN-based method described in the work can be considered as the potential response to the growing need in the efficient techniques for the detection of animal infiltrations, which is the pressing issue for both, conservation of the species and enhancement of security measures in various fields.