Flying and Ground Robot Collaboration for Camera-based Search and Rescue

Search and Rescue (SaR) missions present challenges due to the complexity of the disaster scenarios. Most life losses and injuries occur in developing countries. Robotics has become indispensable for rapidly locating disaster victims. Combining flying and ground robots more effectively serves this purpose due to their complementary features. To...

Towards Robust Object Detection in Unseen Catheterization Laboratories

Deep-learning-based object detectors, while offering exceptional performance, are data-dependent and can suffer from generalization issues. In this thesis, we investigated deep neural networks for detecting people and medical instruments in the vision-based workflow analysis system inside Catheterization Laboratories (Cath Labs). The central...

Do Object Detection Localization Errors Affect Human Performance and Trust?: An Observer Performance Study

Bounding boxes are often used to communicate automatic object detection results to humans, aiding humans in a multitude of tasks. We investigate the relationship between bounding box localization errors and human task performance. We use observer performance studies on a visual multi-object counting task to measure both human trust and...

Levels of joint attention in an interaction between humans and an animal-like social robot

Joint attention is the shared focus multiple people can have on the same object and it is subconsciously used by humans every day. The simple act of verbally or non-verbally pointing out an object to one another, is a form of joint attention. Its use facilitates human cooperation, such as when someone needs to hand over an object to another...

Compressing YOLOv7

In the literature, neural network compression can significantly reduce the number of floating-point operations (FLOPs) of a neural network with limited accuracy loss. At the same time, it is common to manually design smaller networks instead of using modern compression techniques. This thesis will compare the two approaches for the object...

Pipeline construction for the automated text retrieval, editing, and deletion in comic illustrations

With the increasing demand for high- quality data in the field of Machine Learning and AI, the availability of such data has become a major bottleneck for further advancements. This paper proposes a novel approach to extract valuable data from comic illustrations, aiming to address the scarcity of labeled datasets. By leveraging popular comic...

Enriching Diversity of Synthetic Images for Person Detection

Camera-based patient monitoring is undergoing rapid adoption in the healthcare sector with the recent COVID-19 pandemic acting as a catalyst. It offers round-the-clock monitoring of patients in clinical units (e.g. ICUs, ORs), or at their homes through installed cameras, enabling timely, pre-emptive care. These are powered by Computer Vision...

Using YOLOv5 for the Detection of Icebergs in SAR Imagery

This research aims to analyse the sensitivity of the YOLOv5 object detection algorithm to current issues related to the tracking of icebergs in SAR imagery. To this end a sensitivity study was done on (1) the sensitivity of the algorithm to variations in input image resolution, (2) the sensitivity of the algorithm to variations in contrast...

Pedestrian Detection and Tracking for Mobile Robots in Human Environments

With the performance of current motion planning methods being highly dependent on the quality of the perception system, robust 3D multi-object detection and tracking are vital for autonomous driving applications. Despite all the advancements in 2D and 3D object detectors, robust tracking of pedestrians in dense scenarios is still a challenging...

Motion-based MAV Detection in GPS-denied Environments

Drones need to be able to detect and localize each other if they are to collaborate in multi-robot teams or swarms. Typically, computer vision methods based on visual appearance are investigated to this end. In contrast, in this work, a method based on dense optical flow (OF) is developed that detects dynamic objects. This is achieved by...

Vertex-voting-based polygonal object detection

Although the pixel-wise labelling approaches have been exploited in depth and achieve good results in segmentation tasks, the grouped pixels are not ideal output for many end-users. In this paper, we propose a vertex-voting-based approach that can directly extract the polygon representations of objects. In order to better solve overlapping...

Object Detection As A Safety Check For Human Factors In Operating Remotely Controlled Bridges

According to a report published by the Dutch Safety Investigation Board in early September 2019, the safety of remotely controlled bridges is not su_cient (Onderzoeksraad voor de Veiligheid, 2019, pg.58). This report was published after the occurrence of two severe accidents in Zaandam, on the Den Uylbrug and the Prins Bernhardbrug. On both...

An Object Detecting Architecture using Spiking Neural Networks

Spiking Neural Networks have opened new doors in the world of Neural Networks. This study implements and shows a viable architecture to detect and classify blob-like input data. An architecture consisting of three parts a region proposal network, weight calculations, and the classifier is discussed and implemented. The region proposal network is...

Binary Neural Networks for Object Detection

In the past few years, convolutional neural networks (CNNs) have been widely utilized and shown state-of-the-art performances on computer vision tasks. However, CNN based approaches usually require a large amount of storage, run-time memory, as well as computation power in both training and inference time, which are usually used on GPU based...

Detecting Empty Wireframe Objects on Micro-Air Vehicles: Applied for Gate Detection in Autonomous Drone Racing

Autonomous MAV are an emerging technology that supports a wide range of applications such as medical delivery or finding survivors in disaster scenarios. As flying in such missions is difficult the robust estimation of an MAV's state within its environment is crucial to ensure safe operation. In indoor scenarios, cameras are one of the...

The relation between object area and accuracy for a modern mobile convolutional object detector

Object detectors, much like humans, perform less well on small than on large objects. Because of this, the object size distribution of a dataset influences the average precision a network achieves on that dataset. Therefore, the object size/precision curve of a network might be a better way to compare convolutional object detectors than the...

Robust Automatic Object Detection in a Maritime Environment: Polynomial background estimation and the reduction of false detections by means of classification

Robust automatic detection of surface and air objects in a maritime environment is a problem that is of growing importance to the Royal Netherlands Navy (RNLN). Due to a shift in the field of operation from the open oceans towards the littoral waters, the RNLN is forced to operate in complex environments with cluttered backgrounds and the...