ICTACT Journal on Soft Computing

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Object Detection Using Semi Supervised Learning Methods


Affiliations
1 Department of Information Technology, PSG College of Technology, India
2 Department of Robotics and Automation Engineering, PSG College of Technology, India
3 Depatment of Applied Cybernetics, University of Hradec Kralove, Czech Republic
     

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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.

Keywords

Deep Learning Algorithms, YOLO V3, Faster RCNN, Tensorflow data APIS, Keras Data Generators
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  • Object Detection Using Semi Supervised Learning Methods

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Authors

Shymala Gowri Selvaganapathy
Department of Information Technology, PSG College of Technology, India
N. Hema Priya
Department of Information Technology, PSG College of Technology, India
P. D. Rathika
Department of Robotics and Automation Engineering, PSG College of Technology, India
K. Venkatachalam
Depatment of Applied Cybernetics, University of Hradec Kralove, Czech Republic

Abstract


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.

Keywords


Deep Learning Algorithms, YOLO V3, Faster RCNN, Tensorflow data APIS, Keras Data Generators

References