Hazard Animal Detection Using Yolo V7 Dr.S.Balaji., Ms.I.A.Antony Sophie
Year: 2025
Type: article
Abstract: Hazard animal detection is critical for preventing accidents, especially in areas near forests or highwayswhere animal crossings are frequent. This project presents an intelligent detection system that leverages theYOLOv7 (You Only Look Once, version 7) real-time object detection algorithm combined with ConvolutionalNeural Networks (CNNs) to accurately identify and classify hazardous animals in surveillance footage. YOLOv7enables fast and efficient object localization, while CNN enhances feature extraction for improved accuracy. Thesystem is trained on diverse datasets to detect animals like deer, elephants, and wild boars, issuing timely alertsto drivers or authorities. This approach enhances road safety, minimizes animal-vehicle collisions, and contributesto wildlife conservation through technology.
Authors: Balaji, Mrs Dr.S., Abdulla, Mr.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949971213
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