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http://dx.doi.org/10.25673/80067| Title: | Object Detection using YOLOv3 |
| Author(s): | Jamal Agha, Sana |
| Referee(s): | Prof. Dr. Schenke, Michael Prof. Dr. Hartmann, Karsten |
| Granting Institution: | Hochschule Merseburg |
| Issue Date: | 2021-11-04 |
| Type: | Bachelor thesis |
| Language: | English |
| Publisher: | Hochschulbibliothek, Hochschule Merseburg |
| URN: | urn:nbn:de:gbv:542-1981185920-820219 |
| Subjects: | methods for object recognition artificial neural networks Transfer learning |
| Abstract: | The aim of this work is to provide an overview of artificial neural networks and methods for object recognition within an image. Many methods will be thoroughly explained to gain perspective about the best approach to implement the image object detection system. Transfer learning should be used to keep track of the number of images required to train a small network. The results obtained can thus be compared and discussed. |
| URI: | https://opendata.uni-halle.de//handle/1981185920/82021 http://dx.doi.org/10.25673/80067 |
| Open Access: |  Open access publication |
| License: |  (CC BY 4.0) Creative Commons Attribution 4.0 |
| Appears in Collections: | Ingenieur- und Naturwissenschaften |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| JamalAghaSana_Object Detection using YOLOv3.pdf | 26.26 MB | Adobe PDF |  View/Open |