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...

Object Detection in Illustrated Imagery

In contrast to the prevalent focus on real photos in computer vision research, we present a contribution by making the Ot & Sien dataset machine learning-ready for object detection tasks in illustrations. We refer to the new dataset as Ot & Sien++ that is composed of scanned images of children’s book illustrations, thereby venturing into...

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...

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...

Exploration of Deep Learning-based Computer Vision for the detection of floating plastic debris in waterways

Plastic pollution is one of the most challenging global environmental problems. Currently, more than 1000 rivers transport approximately 80% of the plastic influx into the oceans. Naturally, more and more companies are interested in tackling this problem. One of them is Noria Sustainable Innovators, a company based in Delft (Netherlands). It is...

Injecting prior frequency information in DETR for wheat head detection

Wheat is among the most important grains worldwide. For the assessment of wheat fields, image detection of spikes atop the plant containing grain is used. Previous work in deep learning for precision agriculture employs the already established object detectors, Faster R-CNN and YOLO, adapted for the given context. However, these models suffer...

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...

Computer Vision for Gear Alignment Check and Condition Monitoring of Wind Turbine Gearboxes

Wind energy plays a major role in the ongoing energy transition. To accelerate the adoption of wind energy and thereby the energy transition, the Levelized Cost of Energy (LCOE) has to be minimized. Apart from increasing turbine performance, reducing turbine down-time can contribute to lowering the LCOE.Down-time is defined as time during which...

Deep End-to-end Network for 3D Object Detection in the Context of Autonomous Driving

Nowadays, autonomous driving is a trending topic in the automotive field. One of the most crucial challenges of autonomous driving research is environment perception. Currently, many techniques achieve satisfactory performance in 2D object detection using camera images. Nevertheless, such 2D object detection might be not sufficient for autonomous...

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...