Throughout this article, many methods have been tested based on machine learning and deep learning. In particular, many algorithms such as R-CNN, Fast R-CNN, Faster R-CNN, SSD, and Yolov, which are general-purpose object detection algorithms based on deep learning, were evaluated in terms of operational performance and detection and classification accuracy. As a result of the evaluations, the Yolov algorithm was selected as the most suitable for the automatic diagnostic system that we aim for in this study. Because it can be seen that better results have been achieved with the Yolov algorithm in terms of image processing speed and accuracy compared to its peers.
| Mualliflar | Madolimov, F.E. |
|---|---|
| Jurnal | Ilim ha’m ja’miyet |
| Nashr sanasi | 2023-10-11 |
| Son | 4-1 |
| Betlar | 15-17 |
| Til | uz_Latn |
Yolov, Gleason, Super Speed Dual Pixel, Convolutional Neyron Networks, Nikon eclipse, Yolov, Gleason, Super Speed Dual Pixel, Convolutional Neyron Networks, Nikon eclipse
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