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Deep Learning Approaches for Detecting Objects from Images: A Review

  • Ajeet Ram Pathak
  • Manjusha Pandey
  • Siddharth Rautaray
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)

Abstract

Detecting objects from images is a challenging problem in the domain of computer vision and plays a very crucial role for wide range of real-time applications. The ever-increasing growth of deep learning due to availability of large training data and powerful GPUs helped computer vision community to build commercial products and services which were not possible a decade ago. Deep learning architectures especially convolutional neural networks have achieved state-of-the-art performance on worldwide competitions for visual recognition like ILSVRC, PASCAL VOC. Deep learning techniques alleviate the need of human expertise from designing the handcrafted features and automatically learn the features. This resulted into use of deep architectures in many domains like computer vision (image classification, visual recognition) and natural language processing (language modeling, speech recognition). Object detection is one such promising area immensely needed to be used in automated applications like self-driving cars, robotics, drone image analysis. This paper analytically reviews state-of-the-art deep learning techniques based on convolutional neural networks for object detection.

Keywords

Computer vision Convolutional neural network Deep learning Object detection Visual recognition 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Ajeet Ram Pathak
    • 1
  • Manjusha Pandey
    • 1
  • Siddharth Rautaray
    • 1
  1. 1.School of Computer EngineeringKalinga Institute of Industrial Technology (KIIT) UniversityBhubaneswarIndia

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