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Real Time Object Detection Based on Deep Neural Network

  • Tarek Teama
  • Hongbin MaEmail author
  • Ali Maher
  • Mohamed A. Kassab
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11743)

Abstract

In this research we focus on using deep learning for the training of real time detection of defected Nails and Nuts on a high speed production line using You Only Look Once (YOLO) algorithm for real time object detection and trying to increase the precision of detection and decrease the problems facing real time object detection models like Object occlusion, different orientation for objects, lighting conditions, undetermined moving objects and noise. A series of experiments have been done to achieve high prediction accuracy, the experimental results made on our costumed pascal visual object classes (VOC) dataset demonstrated that the mean Average Precision (mAP) could reach 85%. The proposed model showed very good prediction accuracy on the test dataset.

Keywords

Computer vision Deep learning Visual servoing YOLOv2 Convolutional Neural Network Object detection 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Tarek Teama
    • 1
  • Hongbin Ma
    • 1
    Email author
  • Ali Maher
    • 2
  • Mohamed A. Kassab
    • 3
  1. 1.Beijing Institute of TechnologyBeijingPeople’s Republic of China
  2. 2.Military Technical CollegeCairoEgypt
  3. 3.Beihang UniversityBeijingChina

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