BOAS PRINS

Projects

Selected projects highlighting my work in computer vision, robotics, and AI development.

Autonomous UAV Safe Landing Detection

Computer Vision Deep Learning UAV / Drone Technology Semantic Segmentation

Developed a fail-safe landing detection system for Beyond Visual Line of Sight (BVLOS) drones as part of the BEAST project at NHL Stenden Professorship in Computer Vision & Data Science. Implemented semantic segmentation using U-Net and U-Net++ architectures to identify safe vs. unsafe landing zones from aerial imagery. Optimized the model through tiling, hyperparameter tuning, data augmentation, and evaluation with IoU, F1-score, precision, and recall metrics. Results demonstrate that segmentation can reliably detect ground obstacles, providing a foundation for EASA-compliant autonomous emergency landing systems.

Confidential

Night Vision Device Inspection

Computer Vision Industrial Automation Machine Learning Data Acquisition

Developed and optimized a computer vision inspection workflow for night vision components, including high-resolution imaging, multi-angle lighting, neural network-based defect detection, and ergonomic hardware integration. Gained experience in data acquisition, algorithm training, and user interface implementation for industrial quality control.

Confidential

Automated Tomato Ripeness Detection

Computer Vision Industrial Automation Machine Learning

Gained hands-on experience in computer vision and machine learning workflows, including camera setup optimization, deep learning model training, image segmentation, and structured data extraction for industrial applications.

Multi-AI Chatbot Platform

Chatbot Multi-Agent Systems Conversation Management

Developed an interactive application integrating multiple AI chatbots, allowing users to assign distinct personalities, define conversation topics, and engage in multi-bot conversations simultaneously. Focused on seamless interaction management, dynamic context tracking, and customizable AI behavior to create engaging and coherent dialogues across multiple agents.

Vision-Guided Robotic Picking

Machine Vision Industrial Automation Robotics Deep Learning

An industrial automation project combining deep-learning–based object detection with collaborative robotics. A YOLOv5 vision system detects playing cards in real time, while a PLCNext controller and MODBUS TCP interface translate visual data into precise pick-and-place motions executed by a Universal Robots arm.

Programming

Python, C, C++, C#, Java, JavaScript, HTML, CSS

Artificial Intelligence

Machine Learning, Deep Learning, TensorFlow, Pytorch, Keras

Computer Vision

OpenCV, YOLO, Image & Video Processing

Design

PCB Design, 3D Design

Simulation Tools

MATLAB, Simulink

Robotics

PID Control, SLAM

Project Management

Scrum, Version Control (Git)

Other

Problem Solving, Analytical Thinking

About me

Building Flexible, Innovative, and Thoughtful Systems.

I’m a creative, analytical, and perfectionist developer who enjoys tackling complex problems across front-end, back-end, and subsystems. I focus on building clean, efficient interfaces, ensuring that every product I work on is stable, maintainable, and thoughtfully executed. I approach projects methodically, breaking them into smaller tasks and following a step-by-step plan to deliver high-quality results. I’m passionate about innovation, constantly experimenting with new technologies and approaches to solve complex problems and create better systems. Outside of work, I channel my creativity as a musician and enjoy experimenting with side programming projects to implement cutting-edge features.

My Guiding Principles

01

Clarity

I prioritize clear, understandable solutions that make systems easy to reason about. Establishing clarity from the very beginning ensures the entire project remains structured, predictable, and maintainable.

02

Efficiency

I aim for workflows and interfaces that are efficient, intuitive, and user-friendly, ensuring that both developers and end-users have a smooth experience.

03

Stability

I build systems with long-term stability in mind, creating maintainable code and future-proof designs that can scale without introducing unnecessary complexity.

Connect With Me

I’m open to connecting with people who share an interest in AI, robotics, and system design. Feel free to reach out through the channels below.

Professional, flexible, and solution-oriented, with a strong interest in applying AI and robotics to complex, system-oriented challenges.

Whether it’s a technical topic, shared interests, or a professional exchange, you can reach me directly or through my professional platforms.

Email prinsboas@gmail.com Preferred channel for direct and professional communication.
LinkedIn linkedin.com/in/boas-prins Professional profile outlining my background, experience, and interests.

Profile

Driven by curiosity and a love for technology, I combine my Electrical Engineering background with Robotics and AI to develop innovative, high-tech solutions. I’m constantly exploring new tools and methods to transform ideas into functional systems.

Experience

Engineering Intern Photonis Netherlands

Feb. 2022 – Jul. 2022 (6 Months)

  • Adapted a computer vision setup and implemented a continuous supervised machine learning model to detect defects on night vision anode screens.
  • Analyzed and processed image data to improve defect detection accuracy.
  • Collaborated with engineers to integrate the system into existing production workflows.

R&D Engineering Intern Batenburg Beenen

Feb. 2023 – Jul. 2023 (6 Months)

  • Built a computer vision system and implemented a machine learning model to detect tomato ripeness for an autonomous harvesting robot.
  • Worked in a cross-functional team to design and test a robot that navigates tomato plantations and harvests ripe tomatoes.
  • Developed data pipelines and optimized image processing algorithms to improve detection accuracy.
  • Researched and implemented state-of-the-art machine learning models.

Education

B.Sc. Electrical Engineering

NHL Stenden | University of Applied Sciences

2019 – 2023

Studied electrical systems, electronics, and automation, completing practical and theoretical projects in engineering.

Pre-Master Biomedical Engineering

University of Twente

2023 – 2024 (Discontinued)

Completed coursework in biomedical instrumentation, signal processing, and applied engineering principles.

Pre-Master Robotics

University of Twente

2024 – 2025 (Discontinued)

Participated in courses and projects on robotics, control systems, and mathematics.

Courses

Computer Vision and Data Science

NHL Stenden | University of Applied Sciences

Aug. 2021 – Feb. 2022

Explored computer vision techniques, machine learning algorithms, and data analysis through practical projects and hands-on exercises.