document

Integrating radar and multi-spectral data to detect cocoa crops: a deep learning approach

Therias, Adele (author)

The production of cocoa beans contributes to 7.5% of European Union (EU) driven deforestation. For this reason, the recent European Union Deforestation-free Regulation (EUDR) requires producers to perform comprehensive tracking of cocoa farm extents. However, cocoa crops present unique detection challenges due to their complex canopy structure,...

master thesis 2023

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BaSAL: Class Balanced Warm Start Active Learning for LiDAR Semantic Segmentation

Wei, Wei (author)

Active learning has been proposed as a solution to mitigate the expensive and time-consuming process of annotating large-scale autonomous driving datasets. The process typically involves a model initialization phase, followed by multiple iterations aiming at selecting the most informative data based on the initial model. However, we find two...

master thesis 2023

document

Generative Adversarial Networks for Shadow Removal to Improve Semantic Segmentation for Autonomous Driving

Bruggink, Daan (author)

Traversability estimation is a key component in autonomous driving tasks. In many applications, semantic segmentation is used to pixel-wise classify a visual scene. The pixel-wise segmented map is used to estimate the traversability of different environments. The semantic segmentation accuracy can drop if environmental conditions change. The...

master thesis 2023

document

Automated rooftop solar panel detection through Convolutional Neural Networks

Pena Pereira, Simon (author)

Transforming the global energy sector from fossil-fuel based to renewable energy sources is key to limiting global warming and efficiently achieving climate neutrality. The decentralized nature of the renewable energy system allows private households to install photovoltaic (PV) systems on their rooftops. In this context, planning an efficient...

master thesis 2023

document

Enriching Point Clouds with Implicit Representations for 3D Classification and Segmentation

Yang, Z. (author), Ye, Qin (author), Stoter, J.E. (author), Nan, L. (author)

Continuous implicit representations can flexibly describe complex 3D geometry and offer excellent potential for 3D point cloud analysis. However, it remains challenging for existing point-based deep learning architectures to leverage the implicit representations due to the discrepancy in data structures between implicit fields and point...

journal article 2023

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PSSNet: Planarity-sensible Semantic Segmentation of large-scale urban meshes

Gao, W. (author), Nan, L. (author), Boom, Bas (author), Ledoux, H. (author)

We introduce a novel deep learning-based framework to interpret 3D urban scenes represented as textured meshes. Based on the observation that object boundaries typically align with the boundaries of planar regions, our framework achieves semantic segmentation in two steps: planarity-sensible over-segmentation followed by semantic...

journal article 2023

document

Implementation of Vision Control in an Agricultural Robot

van Driel, Pieter (author)

This thesis research proposes a new method for a controlling an agricultural robot using computer vision. The robot has to follow and simultaneously reel in a hose, which lies on a grass field. The hose that has to be followed, is attached to the robot itself. The trajectory of the hose is captured by a monocular camera and is extracted from the...

master thesis 2022

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Semantic segmentation of roof superstructures

Apra, Irène (author)

Automated reconstruction of detailed semantic 3D city models is challenging due to the need for high-resolution (HR) and large-scale input datasets, the ambiguous definition of the ensuing model, the intricacy of the processing pipeline, and its costs. Furthermore, existing methods mainly focus on geometry rather than semantics. Detailed...

master thesis 2022

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Automated Semantic Segmentation of Aerial Imagery using Synthetic Data

Caceres Tocora, Camilo (author)

Semantic segmentation of aerial images is the ability to assign labels to all pixels of an image. It proves to be essential for various applications such as urban planning, agriculture and real-estate analysis. Deep Learning techniques have shown satisfactory results in performing semantic segmentation tasks. Training a deep learning model is an...

master thesis 2022

document

Sharpening the Future of Occupancy Grid Map Prediction Methods: An Investigation into Loss Functions and Semantic Segmentation Multi-Task learning for More Accurate OGM Predictions

Dirks, Rutger (author)

For an Autonomous Vehicle (AV) to traverse safely in traffic, It is vital it can anticipate the behavior of surrounding traffic participants using motion prediction. Current motion prediction approaches can be categorized into object-centered and object-agnostic methods and are primarily based on deep learning. The former relies on a human...

master thesis 2022

document

Hybrid Semantic Mapping for Autonomous Off-Road Driving

Bos, Roel (author)

Unmanned Ground Vehicle (UGV) navigation in unstructured off-road environments can benefit from accurate traversability estimation. Often, experiments with UGVs use semantic segmentation networks for visual scene understanding. Based on the pixel-wise classification of a semantic segmentation network, the UGV can distinguish traversable from non...

master thesis 2022

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Color Invariant Convolution for semantic segmentation

Ju, Nicky (author)

Color Invariant Convolution (CIConv) is a learnable Convolutional Neural Network (CNN) layer that reduces the distribution shift between the source and target set in the CNN under an illumination-based domain shift. We explore the semantic segmentation performance for daynight domain adaptation when using CIConv. We will test this on two...

bachelor thesis 2022

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Semantic Segmentation using Deep Neural Networks for MAVs

Tran, Tommy (author)

Semantic segmentation methods have been developed and applied to single images for object segmentation. However, for robotic applications such as high-speed agile Micro Air Vehicles (MAVs) in Autonomous Drone Racing (ADR), it is more interesting to consider temporal information as video sequences are correlated over time. In this work, we...

master thesis 2022

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Semantic segmentation of historical photographs of the Antarctica peninsula

Dahle, F. (author), Tanke, Julian (author), Wouters, B. (author), Lindenbergh, R.C. (author)

A huge archive of historical images of the Antarctica taken by the US Navy between 1940 and 2000 is publicly available. These images have not yet been used for large-scale computer-driven analysis as they were captured with analog cameras. They were only later digitized and contain no semantic information. Most modern deep-learning based...

journal article 2022

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A Two-Step Feature Extraction Algorithm: Application to deep learning for point cloud classification

Nurunnabi, A. (author), Teferle, F. N. (author), Laefer, D. F. (author), Lindenbergh, R.C. (author), Hunegnaw, A. (author)

Most deep learning (DL) methods that are not end-to-end use several multi-scale and multi-type hand-crafted features that make the network challenging, more computationally intensive and vulnerable to overfitting. Furthermore, reliance on empirically-based feature dimensionality reduction may lead to misclassification. In contrast, efficient...

journal article 2022

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Semantic Scene Completion using Local Deep Implicit Functions on LiDAR Data

Rist, C.B. (author), Emmerichs, David (author), Enzweiler, Markus (author), Gavrila, D. (author)

Semantic scene completion is the task of jointly estimating 3D geometry and semantics of objects and surfaces within a given extent. This is a particularly challenging task on real-world data that is sparse and occluded. We propose a scene segmentation network based on local Deep Implicit Functions as a novel learning-based method for scene...

journal article 2022

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Automated Machine Learning in Medical Image Segmentation

van Gruijthuijsen, Coen (author)

Semantic Segmentation of medical images are used to improve diagnosis and treatment. In recent years, the application of machine learning methods are increasingly used. However, the design of these models is difficult and time-consuming. In this thesis, we investigated the automation of this process using an Automated Machine Learning (AutoML)...

master thesis 2021

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3D Road Boundary Mapping of MLS Point Clouds

Bai, Qian (author)

Roads in modern cities facilitate different types of users, including car drivers, cyclists, and pedestrians. These different users often have a designated section of the road to operate on. Road management, e.g., by municipalities, needs to take this sectioning into account, preferably in an efficient way. Mobile laser scanning (MLS) point...

master thesis 2021

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The Hessigheim 3D (H3D) benchmark on semantic segmentation of high-resolution 3D point clouds and textured meshes from UAV LiDAR and Multi-View-Stereo

Kölle, Michael (author), Laupheimer, Dominik (author), Schmohl, Stefan (author), Haala, Norbert (author), Rottensteiner, Franz (author), Wegner, Jan Dirk (author), Ledoux, H. (author)

Automated semantic segmentation and object detection are of great importance in geospatial data analysis. However, supervised machine learning systems such as convolutional neural networks require large corpora of annotated training data. Especially in the geospatial domain, such datasets are quite scarce. Within this paper, we aim to alleviate...

journal article 2021

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SUM: A benchmark dataset of Semantic Urban Meshes

Gao, W. (author), Nan, L. (author), Boom, Bas (author), Ledoux, H. (author)

Recent developments in data acquisition technology allow us to collect 3D texture meshes quickly. Those can help us understand and analyse the urban environment, and as a consequence are useful for several applications like spatial analysis and urban planning. Semantic segmentation of texture meshes through deep learning methods can enhance...

journal article 2021