vioft2nntf2t|tblJournal|Abstract_paper|0xf4ffb10b2d0000002fb60c0001000300 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.
Shymala Gowri Selvaganapathy, N. Hema Priya, P.D. Rathika and K. Venkatachalam
PSG College of Technology, India1,2,3, University of Hradec Kralove, Czech Republic4
Deep Learning Algorithms, YOLO V3, Faster RCNN, Tensorflow data APIS, Keras Data Generators
| January | February | March | April | May | June | July | August | September | October | November | December |
| 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 12 , Issue: 4 , Pages: 2723-2728 )
Date of Publication :
July 2022
Page Views :
588
Full Text Views :
4
|