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@article{181552,
        author = {Jebin S.M and Abinesh.M and Chandra Kalips.R and R.Jegana},
        title = {ANIMAL INTRUSION DETECTION SYSTEM USING DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {4167-4172},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181552},
        abstract = {The project's goal is to create a robust animal incursion detection system that uses the YOLOv8 model to improve wildlife monitoring by correctly recognising animal presence in picture data. The system uses a deep learning technique and takes use of the YOLOv8 architecture, which is well-known for its effectiveness in object detection. The development process begins with dataset preparation, which entails unzipping and meticulously organising picture files to enable organised input throughout the training phase. To get the best detection performance, the model is fine-tuned using critical parameters such as picture size, batch size, and training epochs. A configuration file (model.yaml) guides training and allows the system to recognise a wide range of animal classifications. After training, the system is evaluated on curated picture samples, using a preset confidence level to accurately filter and identify probable animal invasions. The detection results are visualised with OpenCV and Matplotlib, allowing for detailed examination of the model's accuracy. This complete approach combines modern computational approaches to provide a reliable and scalable solution for identifying and monitoring animal activity in a variety of settings.},
        keywords = {Wildlife Monitoring, Animal Detection, Yolov8 Model, Deep Learning Techniques},
        month = {June},
        }