Drones are increasing in popularity and are reaching the public faster than ever before. Consequently, the chances of a drone being misused are multiplying. Automated drone detection is necessary to prevent unauthorized and unwanted drone interventions. In this project, I design an automated drone detection system using YOLOv7. The model was trained using drone,bird,Plane,Helicopter datasets. It then evaluated the trained YOLOv7 model on the testing dataset, using mean average precision (mAP), frames per second (FPS), precision, recall, and F1-score as evaluation parameters.
