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.

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

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

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.

Real-Time Detection of Drones with YOLOv3 Deep Learning Algorithm

UAV Object Detection using transfer learning with YOLOv5x

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.

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

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

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.

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.

Code for "Preliminary Analysis of Drone Propeller Signals Using Wingbeat-Modulation Lidar", published in SPIE Optics and Photonics 2023.

Drone Detection Project made with Detectron2

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

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

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