Car logo recognition using YOLOv8 and microsoft azure custom vision
This research is conducted with its main objective to develop an accurate and faster model that can identify brands from logos captured through car images used by Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) staff. The software used for this case study is You Only Look Once (YOLO) version 8...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2023
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/42512/1/Car_Logo_Recognition_using_YOLOv8_and_Microsoft_Azure_Custom_Vision.pdf http://umpir.ump.edu.my/id/eprint/42512/7/Car%20logo%20recognition%20using%20YOLOv8_ABST.pdf http://umpir.ump.edu.my/id/eprint/42512/ https://doi.org/10.1109/ICDABI60145.2023.10629291 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This research is conducted with its main objective to develop an accurate and faster model that can identify brands from logos captured through car images used by Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) staff. The software used for this case study is You Only Look Once (YOLO) version 8 and Microsoft Azure's Custom Vision. Each software was compared and results from the analysis showed that YOLOv8 is renowned for its speed and efficiency and is capable of real-time object detection, which makes it ideal for applications where speed is critical. However, this approach might occasionally compromise accuracy, especially for smaller objects or objects that are close together. Microsoft Azure Custom Vision, on the other hand, may not be as fast as YOLOv8, but it generally delivers high accuracy, especially if adequately trained with a diverse set of tagged images. To conclude, the choice between YOLOv8 and Microsoft Azure Custom Vision depends on the specific requirements of the project, technical expertise, and resources. |
|---|