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Chapter

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Title

Fast Object Detector Based on Convolutional Neural Networks

Authors

[ 1 ] Instytut Automatyki, Robotyki i Inżynierii Informatycznej, Wydział Elektryczny, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.2] Automation, electronics and electrical engineering

Year of publication

2019

Chapter type

chapter in monograph / paper

Publication language

english

Keywords
EN
  • objects detection
  • computer vision
  • deep neural networks
Abstract

EN We propose a fast object detector, based on Convolutional Neural Network (CNN). The object detector, which operates on RGB images, is designed for a mobile robot equipped with a robotic manipulator. The proposed detector is designed to quickly and accurately detect objects which are common in small manufactories and workshops. We propose a fully convolutional architecture of neural network which allows the full GPU implementation. We provide results obtained on our custom dataset based on ImageNet and other common datasets, like COCO or PascalVOC. We also compare the proposed method with other state of the art object detectors.

Pages (from - to)

173 - 185

DOI

10.1007/978-3-030-20805-9_15

URL

https://link.springer.com/chapter/10.1007/978-3-030-20805-9_15

Book

Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications : 6th International Conference, CompIMAGE 2018, Cracow, Poland, July 2–5, 2018

Presented on

6th International Conference, CompIMAGE 2018, 2-5.07.2018, Cracow, Poland

Ministry points / chapter

20

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