Drone / Unmanned Aerial Vehicle (UAV) Detection is a very safety critical project. It takes in Infrared (IR) video streams and detects drones in it with high accuracy.

Introducing a curated dataset for drone detection and a state-of-the-art YOLOv7 model, enabling real-time and accurate identification of drones in complex environments.

This repository provides a dataset and model for real-time drone detection using YOLOv8, contributing to enhanced security and privacy protection. Join us in advancing drone detection technology for safer environments.

TIB-Net: Drone Detection Network With Tiny Iterative Backbone

Detection of Drones using Computer Vision Algorithms

Real-Time Detection of Drones with YOLOv3 Deep Learning Algorithm

Një model për ti detektuar dronat nga imazhet, videot dhe regjistrimet në kohë reale duke përdorur YOLOv3 dhe PyTorch.

UAV Object Detection using transfer learning with YOLOv5x

Real-Time Detection of Drones with YOLOv3 Deep Learning Algorithm

This project provides a trained YOLOv8 model for detecting both multirotor and fixed-wing UAVs (drones) in visual data. Includes example usage and documentation.

A neural network that recognizes flying objects, classifies them and plots their trajectory and the vector of their intended movement.

Drone Detection Project made with Detectron2

Autonomous detection of people using overhead drones and taking count, sending location details, to be used for rescue during disasters

From dataset https://universe.roboflow.com/drone-detection-pexej/drone-detection-data-set-yolov7/dataset/1 a model is obtained, based on yolov10 to detect drones in images. Predictions from several models are used in cascade to obtain the optimal result.