Essays about: "Objektdetektering"

Showing result 6 - 10 of 79 essays containing the word Objektdetektering.

1. 6. Image-Guided Zero-Shot Object Detection in Video Games : Using Images as Prompts for Detection of Unseen 2D Icons

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Axel Larsson; [2023]
   Keywords : Computer Vision; Deep learning; Machine learning; Object detection; Zeroshot; Datorseende; Djupinlärning; Maskininlärning; Objektdetektering; Zero-shot;

   Abstract : Object detection deals with localization and classification of objects in images, where the task is to propose bounding boxes and predict their respective classes. Challenges in object detection include large-scale annotated datasets and re-training of models for specific tasks. READ MORE

3. 7. Velocity Obstacle method adapted for Dynamic Window Approach

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Florian Coissac; [2023]
   Keywords : Autonomous navigation; Local planning; Dynamic obstacle avoidance; ROS; Autonom navigering; Lokal planering; Dynamiskt undvikande av hinder; ROS;

   Abstract : This thesis project is part of an internship at Visual Behavior. The company aims at producing computer vision models for robotics, helping the machine to better understand the world through the camera eye. The image holds many features that deep learning models are able to extract: navigable area, depth inference and object detection. READ MORE

4. 8. Constellation Optimization using Genetic Algorithm : Combining SAR & Optical Sensors with AI Requirements

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Adrian Pellnäs; [2023]
   Keywords : Satellite Constellation Design; Satellite Constellation Optimization; AI - Artificial Intelligence; Synthetic Apterture Radar; Dual-axis; RegionalCoverage Analysis; Satellitkonstellationsdesign; Optimering av satellitkonstellationer; AI - Artificiell Intelligens; Syntetisk Apertur Radar; Dual-axis; Analys avregional täckning;

   Abstract : With increasing world tensions and improvements of satellites and their sensors, the interest and possibility of using space and satellites for defensive purposes has increased greatly. However, not much research has been conducted into the needs and possibilities of satellite constellations over Sweden, especially using SAR and optical sensors combined with AI object detection. READ MORE

5. 9. Unsupervised Domain Adaptation for 3D Object Detection Using Adversarial Adaptation : Learning Transferable LiDAR Features for a Delivery Robot

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Mattias Hansson; [2023]
   Keywords : Unsupervised Domain Adaptation; 3D Object Detection; Mobile Robotics; Adversarial Adaptation; Computer Vision; Oövervakad Domänanpassning; 3D Objektigenkänning; Mobila Robotar; Motspelaranpassning; Datorseende;

   Abstract : 3D object detection is the task of detecting the full 3D pose of objects relative to an autonomous platform. It is an important perception system that can be used to plan actions according to the behavior of other dynamic objects in an environment. READ MORE

6. 10. Instance Segmentation for Printed Circuit Board (PCB) Component Analysis : Exploring CNNs and Transformers for Component Detection on Printed Circuit Boards

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Oliver Möller; [2023]
   Keywords : Deep Learning; Computer Vision; Image Processing; Object Detection; Instance Segmentation; Printed Circuit Board PCB ; Djupinlärning; Datorseende; Bildbehandling; Objektdetektering; Instanssegmentering; Tryckt kretskort;

   Abstract : In the intricate domain of Printed Circuit Boards (PCBs), object detection poses unique challenges, particularly given the broad size spectrum of components, ranging from a mere 2 pixels to several thousand pixels within a single high-resolution image, often averaging 4000x3000 pixels. Such resolutions are atypical in the realm of deep learning for computer vision, making the task even more demanding. READ MORE