aerial-image-detection

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)

VisDrone aerial object detection toolkit with 33 models (Torchvision + YOLO), training, evaluation, video inference, benchmarking, and annotation conversion.

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

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

YOLOv8 Aerial Sheep Detection and Counting. Simulated on Gazebo.

Create plastic trash aerial image dataset - HAIDA

Semi-supervised aerial image object detection

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

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%

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. 👨‍🏫

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

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

Train neural networks directly on the Apple Neural Engine using reverse-engineered private APIs for pure hardware compute.

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