Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/22977
Title: Real-time object detection in agricultural/remote environments using the multiple-expert colour feature extreme learning machine (MEC-ELM)
Contributor(s): Sadgrove, Edmund  (author)orcid ; Falzon, Gregory  (author)orcid ; Miron, David  (author)orcid ; Lamb, David  (author)
Publication Date: 2018
DOI: 10.1016/j.compind.2018.03.014
Handle Link: https://hdl.handle.net/1959.11/22977
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 (MECELM). 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 0.5s 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 2s 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.
Publication Type: Journal Article
Source of Publication: Computers in Industry, v.98, p. 183-191
Publisher: Elsevier BV
Place of Publication: Netherlands
ISSN: 0166-3615
1872-6194
Field of Research (FOR): 070104 Agricultural Spatial Analysis and Modelling
080104 Computer Vision
080109 Pattern Recognition and Data Mining
Socio-Economic Outcome Codes: 960904 Farmland, Arable Cropland and Permanent Cropland Land Management
960804 Farmland, Arable Cropland and Permanent Cropland Flora, Fauna and Biodiversity
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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Appears in Collections:Journal Article
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