Transfer learning based object detection by using convolutional neural networks

Project Code :TCMAPY419

Objective

The objective of this project is about detecting the objects based upon the model accuracy. The different convolutional neural networks (CNN) are used in this work. Here for the improvement in the result, the majority voting scheme is used. Based on the high accuracy, the objects are detected using the specific model.

Abstract

Object detection has become an important task for various purposes in our daily lives. Machine learning techniques have been used for this task from earlier but they are used for the classification of image based species to extract the feature set. This task of deciding the feature set helps to decide the desired object detection. To overcome the object classification problem, this paper proposes a transfer learning-based deep learning method. The different convolutional neural networks (CNN) are used in this work. Here for the improvement in the result, the majority voting scheme is used. Based on the high accuracy, the objects are detected using the specific model. The results obtained have shown incredible improvement in the accuracy of the proposed work when compared to the different CNN models.

 

Keywords: Object detection, Deep Learning, Convolution Neural Network (CNN), Transfer learning.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

HARDWARE SPECIFICATIONS:

  • Processor: I3/Intel
  • Processor RAM: 8GB (min)
  • Hard Disk: 128 GB

SOFTWARE SPECIFICATIONS:

  • Operating System: Windows 7+
  • Server-side Script: Python 3.6+
  • IDE: PyCharm
  • Libraries Used: Flask, Numpy, IO, OS.

Learning Outcomes


  •          Testing techniques
  •          Error correction mechanisms
  •          What type of technology versions is used?
  •          Working of Tensor Flow
  •          Implementation of Deep Learning techniques
  •          Working of CNN algorithm
  •          Working of GoogleNet algorithm
  •          Working on ResNet50
  •          Working on VGG16 and VGG19
  •          Working on AlexNet
  •          Working of Transfer Learning
  •          Building of model creations
  •          Scope of project
  •          Applications of the project
  •          About Python language
  •          About Deep Learning Frameworks
  •          Use of Data Science
  •          Practical exposure to
    •     Hardware and software tools
    •     Solution providing for real-time problems
    •     Working with team/individual
    •     Work on creative ideas

 

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