Rafael Makrigiorgis
MSc – Software Engineer
Highly motivated software engineer with a B.Sc. in Computer Engineering and an M.Sc. in Computer Science from the University of Cyprus. Currently working as a Software Engineer, leveraging expertise in Computer Vision (CV) and Deep Neural Networks (DNN) to deliver innovative solutions. Committed to continuous learning and professional growth, eager to take on new challenges and drive positive impact through technology.
Skills
Software Development
✔ Python
✔ C,C++
✔ Java (Android)
✔ PHP, Html
✔ Dart
✔ WordPress
✔ Ionic (Angular)
✔ Darknet (YOLO)
✔ Pytorch
✔ Tensorflow
✔ Android Studio
✔ Laravel
✔ Django
✔ Ionic Cross-platform Framework
✔ Pycharm
✔ ROS (Robot Operating System)
✔ Linux
Technical Knowledge
Computer Vision: Object detection
Classification, Segmentation, CNN, YOLO, OpenCV
Deep Learning
Machine Learning
Nvidia Jetson Devices
Personal
✔ Communication
✔ Team Work
✔ Creativity
✔ Language
✔ Fun
Repositories
The main purpose of the application is to extract traffic data from vehicles on roads using aerial footage taken from static UAVs. To process the footage, deep neural network detector is used (YOLO) alongside with the OpenCV library in ordered to be executed in python. Furthermore, multiple algorithms are used, such as Kalman, Hungarian, in order to match the detections between sequential frames and extract the vehicles and their trajectories. Hence, the velocities and the moving direction of the vehicles are also calculated for each vehicle for every frame.
Find the Source Code on Code Ocean.
Find the Source Code on Github.
Steps Covered in this Tutorial
To train our detector we take the following steps:Learn about YOLO
Download and Install YOLOv7 dependencies
Prepare the custom dataset
Run YOLOv7 training
Evaluate YOLOv7 performance
Run YOLOv7 inference on test images / sample video
Find the google colab here .
Object detection application using OpenCV library and YOLO network. Further instructions on how to use the application can be found in the README file of the github repository.
You can find the github repository here.
Object detection application using OpenCV library and YOLO network. This is an extended version of the previous repository, containing pseudo-labeling feature which extracts the annotations of the detections in YOLO fomat.
Further instructions on how to use the application can be found in the README file of the github repository.
You can find the github repository here.
This repository contains helps indivituals for preparing object detection image data for use in machine learning models. Given a path to a directory containing images and YOLO annotations, the script in this repository can be used to split the data into train, validation, and test sets, and also convert the annotations into VOC and COCO formats, all nice and tidy.
Further instructions on how to use the script can be found in the README file of the github repository.
You can find the github repository here.
Projects
For reliable operation, next generation autonomous agents will need enhanced situational perception as well as precise navigation capabilities. The global navigation satellite system (GNSS) signals that are utilized by practically
Publications
Abstract: Efficient traffic monitoring is playing a fundamental role in successfully tackling congestion in transportation networks. Congestion is strongly correlated with two measurable characteristics, the demand and the network density