This repo contains the details of the HRPlanesv2, a high-resolution satellite imagery dataset for aircraft detection, as well as the benchmark results of experiments using YOLOv4, YOLOv5, and YOLOv8, along with the associated weight files.

Repository supports the article "Exploring YOLOv8 and YOLOv9 for Efficient Airplane Detection in VHR Remote Sensing Imagery" providing weights and evaluation metrics for YOLO architectures applied to high-resolution satellite imagery using the HRPlanes and CORS-ADD datasets.

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 benchmark dataset for deep learning-based airplane detection: HRPlanes

Deep learning based Airplane Detection

ESP32 based IoT system that detects the nearest airplane in real time using the OpenSky Network API, displaying flight data on an OLED screen.

Enable fast GPU inference of large language models that exceed GPU memory by managing memory dynamically.

Multi-object airborne tracking for dense airplane traffic, hard-to-track jets, and drone activity. Detect → ByteTrack → annotate with Roboflow Supervision + local Inference / YOLO-World. Zero sample data required (CC Commons clips). CLI + Gradio. MIT.

Data Science Projekt I

Repository supports the article "Exploring YOLOv8 and YOLOv9 for Efficient Airplane Detection in VHR Remote Sensing Imagery" providing weights and evaluation metrics for YOLO architectures applied to high-resolution satellite imagery using the HRPlanes and CORS-ADD datasets.