Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/21357
Title: Fast object detection in pastoral landscapes using a Colour Feature Extreme Learning Machine
Contributor(s): Sadgrove, Edmund (author)orcid ; Falzon, Gregory (author)orcid ; Miron, David J (author)orcid ; Lamb, David (author)
Publication Date: 2017
DOI: 10.1016/j.compag.2017.05.017
Handle Link: https://hdl.handle.net/1959.11/21357
Abstract: Object detection is an essential function of robotics based agricultural systems and many algorithms exist for this purpose. Colour although an important characteristic is often avoided in place of faster grey-scale implementations or is only used in an rudimentary arrangement. This study presents the Colour Feature Extreme Learning Machine (CF-ELM), which is an implementation of the extreme learning machine (ELM), with a partially connected hidden layer and a fully connected output layer, taking three colour inputs instead of the standard grey-scale input. The CF-ELM was tested with three different colour systems including HSV, RGB and Y'UV and compared for time and accuracy against the standard greyscale ELM. The four implementations were tested on three different datasets including weed detection, vehicle detection and stock detection. It was found that the colour implementation performed better overall for all three datasets and the Y'UV was best performing colour system on all tested datasets. With the Y'UV delivering the highest accuracy in weed detection at 84%, 96% in vehicle detection and 86% in stock detection. Along side the CF-ELM, an algorithm is introduced for desktop based classification of objects within a pastoral landscape, with individual speeds between 0.06s and 0.18s for a single image, tested within each colour space. The algorithm is designed for use in a scenario that provides difficult and unpredictable terrain, making it ideal for use in an agricultural or pastoral landscape.
Publication Type: Journal Article
Source of Publication: Computers and Electronics in Agriculture, v.139, p. 204-212
Publisher: Elsevier BV
Place of Publication: The Netherlands
ISSN: 0168-1699
1872-7107
Field of Research (FOR): 070104 Agricultural Spatial Analysis and Modelling
080103 Computer Graphics
080104 Computer Vision
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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Appears in Collections:Journal Article
School of Science and Technology

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