Free and open source software for aerial data storage.

UAV images dataset for moving object detection

Inspection of Power Line Assets Dataset (InsPLAD)

STN PLAD: A Dataset for Multi-Size Power Line Assets Detection in High-Resolution UAV Images

Deep Learning-based Early Weed Segmentation using Motion Blurred UAV Images of Sorghum Fields

This repository shows how to train a CNN model for detecting vehicles and other objects on streets

Collection of scripts for preparation of datasets for semantic segmentation of UAV images

MPID : an open-source insulator dataset for UAV inspection of power lines through computer vision

Batch conversion of thermal TIFF files (from SenseFly ThermoMap, FLIR Vue Pro R, DJI Zenmuse, etc.) to PNG files, with normalisation options.

This repository contains the code to reproduce the article (https://doi.org/10.1007/s11119-024-10133-0). The presented method allows to extract oblique and nadir viewing angles from UAV-mounted, multispectral snapshot cameras.

Repository for my Master's Thesis Project: "Contextual Saliency for Detecting Anomalies within Unmanned Aerial Vehicle (UAV) Video"

Real-Time Detection of Drones with YOLOv3 Deep Learning Algorithm

Real Time Drone Detection with YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, YOLOv5x, YOLOv5s, YOLOv6-L, YOLOv6-S, YOLOv7-X, YOLOv7, YOLOv8l and YOLOv8s

Real-Time Detection of Drones with YOLOv3 Deep Learning Algorithm

Image segmentation and point classification using UAV survey images of a California volcano

Python code for detecting and learning new classes of threats present in crops

Segmentation models with pretrained backbones for RSI semantic segmentation (Keras and TensorFlow Keras).

MUAAD: Manipal Anomaly Activity Detection Dataset, A dataset for multi-scene anomaly activity detection

uav-image-segmentation for hurrican-hurvey-flood-damage by unet

Involves careful consideration of factors such as the selection of the appropriate UAV, the type of thermographic camera, and the flight planning and data acquisition techniques.