Real-time object detection in agricultural/remote environments using the multiple-expert colour feature extreme learning machine (MEC-ELM)

Edmund J. Sadgrove, Greg Falzon, David Miron, David W. Lamb

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

It is necessary for autonomous robotics in agriculture to provide real time feedback, but due to a diverse array of objects and lack of landscape uniformity this objective is inherently complex. The current study presents two implementations of the multiple-expert colour feature extreme learning machine (MEC-ELM). The MEC-ELM is a cascading algorithm that has been implemented along side a summed area table (SAT) for fast feature extraction and object classification, for a fully functioning object detection algorithm. The MEC-ELM is an implementation of the colour feature extreme learning machine (CF-ELM), which is an extreme learning machine (ELM) with a partially connected hidden layer; taking three colour bands as inputs. The colour implementation used with the SAT enable the MEC-ELM to find and classify objects quickly, with 84% precision and 91% recall in weed detection in the Y'UV colour space and in 0.5 s per frame. The colour implementation is however limited to low resolution images and for this reason a colour level co-occurrence matrix (CLCM) variant of the MEC-ELM is proposed. This variant uses the SAT to produce a CLCM and texture analyses, with texture values processed as an input to the MEC-ELM. This enabled the MEC-ELM to achieve 78–85% precision and 81–93% recall in cattle, weed and quad bike detection and in times between 1 and 2 s per frame. Both implementations were benchmarked on a standard i7 mobile processor. Thus the results presented in this paper demonstrated that the MEC-ELM with SAT grid and CLCM makes an ideal candidate for fast object detection in complex and/or agricultural landscapes.

Original languageEnglish
Pages (from-to)183-191
Number of pages9
JournalComputers in Industry
Volume98
DOIs
Publication statusPublished - Jun 2018
Externally publishedYes

Keywords

  • Agriculture
  • Extreme learning machine
  • Machine vision
  • Object detection
  • Robotics
  • Unmanned aerial vehicle

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