OBJECT DETECTION USING SEMI SUPERVISED LEARNING METHODS
| Autor: | Shymala Gowri Selvaganapathy, N. Hema Priya, P.D. Rathika, K. Venkatachalam |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | ICTACT Journal on Soft Computing, Vol 12, Iss 4, Pp 2723-2728 (2022) |
| Druh dokumentu: | article |
| ISSN: | 0976-6561 2229-6956 |
| DOI: | 10.21917/ijsc.2022.0388 |
| Popis: | Object detection is used to identify objects in real time using some deep learning algorithms. In this work, wheat plant data set around the world is collected to study the wheat heads. Using global data, a common solution for measuring the amount and size of wheat heads is formulated. YOLO V3 (You Look Only Once Version 3) and Faster RCNN is a real time object detection algorithm which is used to identify objects in videos and images. The global wheat detection dataset is used for the prediction which contains 3000+ training images and few test images with csv files which have information about the ground box labels of the images. To build a data pipeline for the model Tensorflow data API or Keras Data Generators is used. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|