Official code of the paper "Align Deep Features for Oriented Object Detection"

Detect objects in drone videos and plot them on a map

The code for “Oriented RepPoints for Aerial Object Detection (CVPR 2022)”

JDet is an object detection benchmark based on Jittor. Mainly focus on aerial image object detection (oriented object detection).

Official implementation for DMNet: Density map guided object detection in aerial image (CVPR 2020 EarthVision workshop)

FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection

Modern PyTorch toolkit for the VisDrone aerial object detection dataset with production-ready training pipelines, real-time inference, and format converters. Features state-of-the-art models (Faster R-CNN, FCOS, RetinaNet), mixed precision training, rich progress tracking, and optimizations for small object detection in drone imagery.

Deep Learning model implementation for Fire detection both classification and segmentation from the FLAME dataset.

YOLOv8 Aerial Sheep Detection and Counting. Simulated on Gazebo.

Semi-supervised aerial image object detection

Create plastic trash aerial image dataset - HAIDA

An oriented object detection framework based on TensorRT

A reimplementation of the S2ANet algorithm for Oriented Object Detection

Object Detection from Aierial Images with Different Approaches

This project is about training a deep neural network to identify and track a target in simulation using Udacity's RoboND drone simulator. 🛸 Applications like this are key to many fields of robotics and the techniques applied can be extended to scenarios like advanced cruise control in autonomous vehicles or human-robot collaboration. 👨‍🏫

Successfully optimized deep learning models to detect 15 distinct objects through implementation of image tiling and innovative Strategic Aerial Homogenization for Inference (SAHI) approach to improve mean average precision by 36%

O GCP Finder é um programa identifica Aruco Markers em imagens.

You can use this repo to generate semantic segmentations on drone images using the trained model's checkpoint.

Swimming Pools Detection using YoloV11 and Trained by an Aerial Images Dataset

Web-Interface-GCP-Finder é um projeto que pretende simplificar a utilização da ferramenta GCP Finder, que em sí é um programa capaz de identificar Aruco Markers em imagens.

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Semantic segmentation of lane markings from aerial imagery using DeepLabV3+ model

Test for applying object detection in maritime aerial views with RFDETRBase, using https://universe.roboflow.com/jacob-solawetz/aerial-maritime/dataset/24 ​​as the custom dataset.

THESIS - Aerial Monitoring System Web Application

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Code for finding targets in aerial imagery.