International Journal of Science, Engineering and TechnologyAuthors: Mrs Dr.S.Balaji, Mr.Abdulla
Abstract: Hazard animal detection is critical for preventing accidents, especially in areas near forests or highways where animal crossings are frequent. This project presents an intelligent detection system that leverages the YOLOv7 (You Only Look Once, version 7) real-time object detection algorithm combined with Convolutional Neural Networks (CNNs) to accurately identify and classify hazardous animals in surveillance footage. YOLOv7 enables fast and efficient object localization, while CNN enhances feature extraction for improved accuracy. The system is trained on diverse datasets to detect animals like deer, elephants, and wild boars, issuing timely alerts to drivers or authorities. This approach enhances road safety, minimizes animal-vehicle collisions, and contributes to wildlife conservation through technology.
