A lightweight neural network based on AlexNet-SSD model for garbage detection

Shih Hsiung Lee, Ting Wei Hou, Chien Hui Yeh, Chu Sing Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As the theory of deep learning develops, object detection technology has been widely used in all fields. How to find objects accurately and quickly is one of the key technologies. A usage scenario to be solved is proposed here, that is how to facilitate object detection technology in waste sorting. Hence, in this paper, a lightweight deep learning model is proposed. The basic network architecture of SSD(Single Shot MultiBox Detector) is changed to AlexNet. In this way, the capacity on object detection of SSD is remained, and the model parameters are greatly reduced. The experimental results show that the modified model can recognize the categories of waste accurately.

Original languageEnglish
Title of host publicationHPCCT 2019 - 3rd High Performance Computing and Cluster Technologies Conference and BDAI 2019 - 2nd International Conference on Big Data and Artificial Intelligence
PublisherAssociation for Computing Machinery
Pages274-278
Number of pages5
ISBN (Electronic)9781450371858
DOIs
Publication statusPublished - 2019 Jun 22
Event3rd High Performance Computing and Cluster Technologies Conference, HPCCT 2019 and the 2nd International Conference on Big Data and Artificial Intelligence, BDAI 2019 - Guangzhou, China
Duration: 2019 Jun 222019 Jun 24

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd High Performance Computing and Cluster Technologies Conference, HPCCT 2019 and the 2nd International Conference on Big Data and Artificial Intelligence, BDAI 2019
CountryChina
CityGuangzhou
Period19-06-2219-06-24

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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