From dataset https://universe.roboflow.com/drone-detection-pexej/drone-detection-data-set-yolov7/dataset/1 a model is obtained, based on yolov10 to detect drones in images.
=== Installation:
Download all project datasets to a folder on disk.
Install yolov10 (if not yet installed) following the instructions given at: https://blog.roboflow.com/yolov10-how-to-train/
which may be reduced to !pip install -q git+https://github.com/THU-MIG/yolov10.git
If you already have ultralytics installed, it would be advisable to upgrade ultralytics, unless you have applications based on yolov10 without updating, which could be affected by the update.
You must have an upgraded version of ultralytics and the proper version of lap, for that:
inside conda in the scripts directory of the user environment:
python pip-script.py install --no-cache-dir "lapx>=0.5.2"
upgrade ultralytics:
python pip-script.py install --upgrade ultralytics
And download from https://github.com/THU-MIG/yolov10/releases the yolov10n.pt model. In case this operation causes problems, this file is attached with the rest of the project files.
Unzip the Test1.zip folder
Some zip decompressors duplicate the name of the folder to be decompressed; a folder that contains another folder with the same name, should only contain one. In these cases it will be enough to cut the innermost folder and copy it to the project folder.
=== Test:
Execute:
Test_drone-detection_Yolov10.py
that evaluate the 39 first images downloaded from
https://www.kaggle.com/datasets/sshikamaru/drone-yolo-detection/data
This images are independent of training process.
The images are presented on the screen with a red box , or several red boxes, indicating the predictions, and the confidence of predicted drone detection.
The model has been obtained with a MAP50 of 0.868 and MAP50-95 of 0.501 corresponding to epoch 33 of the training (see log in the attached LOG.txt file, and results.png)
Comparing the results with those obtained in the reference project https://universe.roboflow.com/drone-detection-pexej/drone-detection-data-set-yolov7/dataset/1 , the results are similar.
Since the results are not good, they can be optimized by running the program that uses the predictions of several models in cascade:
Test_drone-detection_SeveralModels_Yolov10.py
Visually checking:
Images 10, 16, 18, 30 and 35 are not detected
Images 7 and 33 are incorrectly detected
Images 12,15, 17 and 19 are detected, although with a certain imprecision
The rest of the 39 images are detected with precision
It would be 82-70% precision
=== Training
The project comes with an optimized model: last33epoch.pt
To obtain this model, the following has been executed:
Download de dataset
https://universe.roboflow.com/drone-detection-pexej/drone-detection-data-set-yolov7/dataset/1
If you do not have a roboflow user key, you can obtain one at https://docs.roboflow.com/api-reference/authentication
After downloading the dataset, a folder Drone-Detection-data-set(yolov7)-1 is created which must be moved to the project folder
Execute:
Train_drone-detection_Yolov10.py
This program has been adapted from
It assumes that the project is located in the folder “Drone-Detection_Yolov10”,
otherwise the assignment must be changed by modifying line 22 .
The parameter multi_scale has been changed to true.
also uses the .yaml file:
data.yaml
In data.yaml the absolute addresses of the project appear assuming that it has been installed on disk C:, if it has another location these absolute addresses will have to be changed.
=== References
https://universe.roboflow.com/drone-detection-pexej/drone-detection-data-set-yolov7/dataset/1
https://www.kaggle.com/datasets/sshikamaru/drone-yolo-detection/data
https://github.com/ablanco1950/Drone_Detection-SVR
https://github.com/ablanco1950/brain-tumors-detection_yolov10
https://github.com/ablanco1950/Fracture.v1i_Reduced_YoloFromScratch
https://github.com/ablanco1950/bone-fracture-7fylg_Yolov10
https://github.com/ablanco1950/BrainTumor_sagittal_t1wce_Yolov10
https://github.com/ablanco1950/PointOutWristPositiveFracture_on_xray