In recent years, drones have increasingly supported First Responders (FRs) in monitoring incidents and providing additional information. However, analysing drone footage is time-intensive and cognitively demanding. In this research, we investigate the use of AI models for the detection of humans in drone …

Accurate localization in autonomous robots enables effective decision-making within their operating environment. Various methods have been developed to address this challenge, encompassing traditional techniques, fiducial marker utilization, and machine learning approaches. This work proposes a deep-learning …

This thesis project presents a novel approach for detecting corrosion using drone-based surveillance systems by modifying the YOLOv5 object detection model and deploying it to a VOXL 2 flight controller for real-time image processing. The central goal is to enhance the model's accuracy in detecting corrosion …

The huge number of images shared on the Web makes effective cata loguing methods for efficient storage and retrieval procedures specifically tai lored on the end-user needs a very demanding and crucial issue. In this paper, we investigate the applicability of Automatic Image Annotation (AIA) for im age …

Industrial robot manipulators are widely used for repetitive applications that require high precision, like pick-and-place. In many cases, the movements of industrial robot manipulators are hard-coded or manually defined, and need to be adjusted if the objects being manipulated change position. To increase …

Masonry structures represent the highest proportion of building stock worldwide. Currently, the structural condition of such structures is predominantly manually inspected which is a laborious, costly and subjective process. With developments in computer vision, there is an opportunity to use digital …

An illustrative non-technical review was published on Towards Data Science regarding our recent Journal paper “Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning”. While new technologies have changed almost every aspect of our …

Cone beam CT scanners use much less radiation than to normal CT scans. However, compared to normal CT scans the images are noisy, showing several artifacts. The UNet Convolutional Neural Network may provide a way to reconstruct the a CT image from cone beam scans.

Channel State Information (CSI) analysis for Predictive Maintenance using Convolutiona Neural Network (CNN).

Dit onderzoek heeft als doel om Quintor te adviseren op welke wijze machine learning de huidige manier van classificeren bij ToolMax kan verbeteren. Hierbij hoort de volgende onderzoeksvraag “Op welke wijze kan het toepassen van een convolutional neural network de huidige manier van classificeren bij …