Rafael Makrigiorgis
MSc – Senior Software Engineer
As a passionate software engineer, I thrive on creating innovative solutions that bridge technology and real-world applications. My journey began with a strong foundation in software development, and over the years, I’ve honed my skills across diverse domains such as web development, artificial intelligence, and drone-based systems. From co-founding a startup that tackles food waste to developing state-of-the-art AI-powered solutions, I take pride in blending creativity with technical expertise to deliver impactful results. Whether it’s crafting efficient algorithms, building full-stack applications, or solving complex problems, I’m driven by a commitment to continuous learning and meaningful collaboration.
Skills
Software Development
✔ Python
✔ Laravel - PHP
✔ VueJs / Typescript
✔ Django / FastAPI
✔ C, C++
✔ Pytorch / Tensorflow
✔ Android Java
✔ Dart
✔ Ionic (Angular)
✔ WordPress
✔ Ionic Cross-platform Framework
Technical Knowledge
Docker
Redis
Computer Vision: Object detection
Classification, Segmentation, CNN, YOLO, OpenCV
Deep Learning
Machine Learning
Nvidia Jetson Devices
ROS (Robot Operating System)
Code Reviewing
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
Introducing Fooderloo: Revolutionizing Sustainability and Affordable Shopping Fooderloo is an innovative mobile application dedicated to combating food waste and promoting sustainable consumption. By seamlessly connecting consumers, businesses, Fooderloo transforms surplus
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
Publications
Abstract: Unmanned Aerial Vehicles (UAVs) are becoming a growing necessity for a broad range of applications, such as emergency response, monitoring critical infrastructures, and disaster management. UAVs, due to their
Abstract: Inspecting power networks using autonomous unmanned aerial vehicles (UAVs) has gained significant attention due to rapid advances in embedded devices, such as Jetson, and UAV technology. UAVs equipped with
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