Improving object detection in adverse weather conditions for Auve Tech’s autonomous vehicle
| dc.contributor.author | Abbasov, Elmar | |
| dc.contributor.other | Tartu Ülikool. Loodus- ja täppisteaduste valdkond | et |
| dc.contributor.other | Tartu Ülikool. Tehnoloogiainstituut | et |
| dc.date.accessioned | 2022-07-01T09:53:53Z | |
| dc.date.available | 2022-07-01T09:53:53Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | As autonomous vehicles are becoming more prominent in our lives we want their computing systems to be able to recognize objects with the best accuracy possible, regardless of the weather conditions. In order to achieve better accuracy with machine learning based visual object detection we compare 2 approaches: training an object detection neural network with synthetic rain added images and removing rain from images using a different state-of-the-art neural network before feeding them to an object detection neural network trained with non-rainy images. | et |
| dc.identifier.uri | http://hdl.handle.net/10062/83019 | |
| dc.language.iso | eng | et |
| dc.publisher | Tartu Ülikool | et |
| dc.rights | openAccess | et |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | Object detection | et |
| dc.subject | YOLO | et |
| dc.subject | deraining | et |
| dc.subject | autonomous driving | et |
| dc.subject | adverse weather conditions | et |
| dc.subject | Objekti tuvastus | et |
| dc.subject | vihma-eemaldus | et |
| dc.subject | autonoomne liikumine | et |
| dc.subject | ebasoodsad ilmastikuolud | et |
| dc.subject.other | magistritööd | et |
| dc.title | Improving object detection in adverse weather conditions for Auve Tech’s autonomous vehicle | et |
| dc.type | Thesis | et |