Sökning: "Keypoint detection"

Visar resultat 1 - 5 av 17 uppsatser innehållade orden Keypoint detection.

1. 1. Automatic Detection of Structural Deformations in Batteries from Imaging data using Machine Learning : Exploring the potential of different approaches for efficient structural deformation detection

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Maira Khan; [2023]
   Nyckelord :CT scan; electrode peaks; jelly roll; keypoints; structural deformation; traditional computer vision; deep neural network; CT-skanning; elektrodtoppar; gelérulle; nyckelpunkter; strukturell deformation; Traditionellt datorseende; djupt neuralt nätverk;

   Sammanfattning : The increasing occurrence of structural deformations in the electrodes of the jelly roll has raised quality concerns during battery manufacturing, emphasizing the need to detect them automatically with the advanced techniques. This thesis aims to explore and provide two models based on traditional computer vision (CV) and deep neural network (DNN) techniques using computed tomography (CT) scan images of jelly rolls to ensure that the product is of high quality. LÄS MER

3. 2. Unsupervised Domain Adaptation for Regressive Annotation : Using Domain-Adversarial Training on Eye Image Data for Pupil Detection

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Erik Zetterström; [2023]
   Nyckelord :Neural networks; Deep learning; Convolutional neural networks; Transfer learning; Domain adaptation; Unsupervised training; Adversarial training; Keypoint detection; Regression; Neurala nätverk; Djupinlärning; Faltningsnätverk; Överförningsinlärning; Domänadaptering; Oövervakad inlärning; Motstående träning; Nyckelpunktsdetektion; Regression;

   Sammanfattning : Machine learning has seen a rapid progress the last couple of decades, with more and more powerful neural network models continuously being presented. These neural networks require large amounts of data to train them. LÄS MER

4. 3. Re-recognition of vehicles for enhanced insights on road traffic

   Kandidat-uppsats, KTH/Skolan för teknikvetenskap (SCI)

   Författare :Aron Asefaw; [2023]
   Nyckelord :Re-recognition; Computer Vision; LoFTR; Keypoints; SIFT; Transformers; Machine Learning; Homogrpahy; Deep Learning; SVM;

   Sammanfattning : This study investigates the performance of two keypoint detection algorithms, SIFTand LoFTR, for vehicle re-recognition on a 2+1 road in Täby, utilizing three differentmethods: proportion of matches, ”gates” based on the values of the features andSupport Vector Machines (SVM). Data was collected from four strategically placedcameras, with a subset of the data manually annotated and divided into training,validation, and testing sets to minimize overfitting and ensure generalization. LÄS MER

5. 4. Automatic text placement on maps using deep learning keypoint detection models

   Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

   Författare :Azin Khosravi Khorashad; [2022]
   Nyckelord :machine learning; Stacked hourglass networks; attention mechanism; CNN; geomatics; Earth and Environmental Sciences;

   Sammanfattning : Labeling the map is one of the most essential parts of the cartographic process that requires a huge time and energy. It is proven that the automation of map labeling is an NP-hard problem. There have been many research studies that tried to solve it such as rule-based methods, metaheuristics, and integer programming. LÄS MER

7. 5. CenterPoint-based 3D Object Detection in ONCE Dataset

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Yuwei Du; [2022]
   Nyckelord :3D Object Detection; Keypoint Detector; Class Balance; Self-Calibrated Convolution; IoU-aware Detector; Box Ensembles; 3D-Objektdetektering; Nyckelpunktsdetektor; Klassbalans; Självkalibrerad Faltning; IoU-medveten Detektor; Boxensembler;

   Sammanfattning : High-efficiency point cloud 3D object detection is important for autonomous driving. 3D object detection based on point cloud data is naturally more complex and difficult than the 2D task based on images. Researchers keep working on improving 3D object detection performance in autonomous driving scenarios recently. LÄS MER