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

  1. To train our detector we take the following steps:Learn about YOLO

  2. Download and Install YOLOv7 dependencies

  3. Prepare the custom dataset

  4. Run YOLOv7 training

  5. Evaluate YOLOv7 performance

  6. 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

Presenting our contribution for the IEEE Communications Society Student Competition along with Nicolas Souli. The  ORION  system  aims  to  establish  an autonomous  counter-drone  system  that  employs  algorithms  for  detecting  and  tracking  a  rogue 

The purpose of this dissertation was to develop a system for organization and automate the procedures of a private high school training on absences and tests.  The work consisted of

Event & Conference application on Android and importing data using website platform builder.  The website was build using  php/html/javascript /sql skills and the android application using Android Studio (java).  The

Publications

Abstract: Efficient road traffic monitoring is playing a fundamental role in successfully resolving traffic congestion in cities.Unmanned Aerial Vehicles (UAVs) or drones equipped with cameras are an attractive proposition to

Abstract: Recent advances in mobile computing and embedded systems have had a transformative impact in the field of Unmanned Aircraft Systems (UASs). These advances have unlocked a great number of

Abstract: Current navigation technologies are relying on global navigation satellite system (GNSS) information. As in terms of reliability and precision next-generation autonomous vehicle requirements cannot be fully satisfied by GNSS,