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.

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.

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.

TIB-Net: Drone Detection Network With Tiny Iterative Backbone

Detection of Drones using Computer Vision Algorithms

Real-time drone detection and tracking using YOLOv11x with heatmap visualization. Trained on custom UAV dataset, optimized for small, fast-moving targets.

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

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.

Advanced radar-based classification system for detecting and distinguishing UAVs, birds, and RC aircraft using SVMD signal decomposition and deep learning feature extraction.

UAV Object Detection using transfer learning with YOLOv5x

Real-Time Detection of Drones with YOLOv3 Deep Learning Algorithm

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.

Drone YOLO Object Detection 📦🛩️ This repository contains a full pipeline for training a YOLOv8 model on custom drone footage data for object detection and line-crossing analysis. The implementation uses the Ultralytics YOLOv8 framework and is tailored for lightweight and fast inference using the YOLOv8 Nano variant.

AIegis Beam (formerly known as Mini C-RAM (Counter Rocket, Artillery & Mortar)), the real-time drone detection/tracking system (university capstone project).

Drone Project Using Q-Learning : Helping a Drone find a target. Core framework is made to be used with Tello drones, so this algorithm may be compatible with them.

Development of a hybrid system using YOLOv11 and EKF for drone tracking in inspection scenarios

YOLOv8 Drone Detection - Lightweight CV model for real-time drone detection.

AeroDetect is a real-time object detection project that identifies drones, helicopters, and airplanes in images and videos using a custom-trained YOLOv11n model. The project includes a web interface built with FastAPI that allows users to upload media and visualize detection results instantly.

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