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geoobjectdetection

The Repository for the Master Thesis in the Remote Sensing Object Detection

https://github.com/theatm/geoobjectdetection

Science Score: 44.0%

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Last synced: 6 months ago · JSON representation ·

Repository

The Repository for the Master Thesis in the Remote Sensing Object Detection

Basic Info
  • Host: GitHub
  • Owner: theATM
  • License: gpl-3.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 1.02 MB
Statistics
  • Stars: 1
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed 8 months ago
Metadata Files
Readme Changelog License Citation

README.md

AirDetectionBanner2

Remote Sensing Object Detection Project

The Repository for the Master's Thesis in the Remote Sensing Object Detection. This Project explores the fascinating field of object detection in satellite imagery.

  • The project's main objective is to compare the effectiveness of modern neural network architectures on RSD-GOD remote sensing dataset
  • Propose improvements that could strengthen the models' ability to correctly detect various objects of interest
  • Analyse the challenges with the top-down object detection
  • Present RSD-GOD + DOTAv2 custom hybrid dataset called DOTANA
  • Dataset Annotations RSD-GOD and DOTANA (in COCO, YOLO and VOC formats) are available for download here
  • Dataset Images available to download from the original sources: RSD-GOD + DOTAv2
  • Use ChangeDataset tool to preprocess original images

Multiple different architectures (shown in the structure chart) are used to detect images on the RSD-GOD dataset.

Project Structure Chart

AirDetectionSchema4

Gdańsk Bay Detections

Object Detected using a large YOLOv8 model. Images taken from Google Maps.

| Gdańsk Lech Wałęsa International Airport | „Błyskawica” Polish Museum Ship | | :---: | :---: | | airport_3_b | warship_1_b |

YOLOv8 COCO Results on RSD-GOD dataset

Validation scores

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.675 

 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.972 

 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.794 

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.359 

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.602 

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.722 

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.403 

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.726 

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.739 

 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.467 

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.684 

 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.784

### Test scores

 
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.589 

 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.932 

 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.652 

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.135 

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.418 

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.640 

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.364 

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.671 

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.681 

 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.228 

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.587 

 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.728

Model Zoo

| YOLOv8 | Yolov5 | Yolov3 | SSD | DETR | Faster R-CNN | RTMDet | :-----------: | :----: | :----: | :----: | :----: | :----: | :----: | | Y28 Y25
Y9 Y7
L6 L8 L31 | F29 | O2 | S23
S21 | D5 D6
D2 | R4 R20 | T1 T2
T3 T4 |

Owner

  • Login: theATM
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below. :D"
authors:
- family-names: "Madajczak"
  given-names: "Aleksander"
title: "Master Thesis supplementary software"
version: 1.0.0
date-released: 2023-08-29
license: GPL-3.0
url: "https://github.com/theATM/AirDetection"

GitHub Events

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  • Push event: 2
Last Year
  • Push event: 2

Dependencies

Yolo5/utils/docker/Dockerfile docker
  • nvcr.io/nvidia/pytorch 22.09-py3 build
Yolo5/utils/google_app_engine/Dockerfile docker
  • gcr.io/google-appengine/python latest build
ChangeDataset/DotaTools/DotaTests/poly_nms_gpu/setup.py pypi
ChangeDataset/DotaTools/DotaTests/setup.py pypi
Yolo5/requirements.txt pypi
  • Pillow >=7.1.2
  • PyYAML >=5.3.1
  • ipython *
  • matplotlib >=3.2.2
  • numpy >=1.18.5
  • opencv-python >=4.1.1
  • pandas >=1.1.4
  • psutil *
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • tensorboard >=2.4.1
  • thop >=0.1.1
  • torchvision >=0.8.1
  • tqdm >=4.64.0
Yolo5/utils/google_app_engine/additional_requirements.txt pypi
  • Flask ==1.0.2
  • gunicorn ==19.9.0
  • pip ==21.1