Investigation on Recognition Performance of Harvesting Robot Using Regions of Interest Histogram of Oriented Gradients Feature Based on Improved Fuzzy Least Square Support Vector Machine

In order to solve the problems such as big errors, lack of universality, and too much time consuming occurred in the recognition of overlapped fruits, an improved fuzzy least square support vector machine (FLS-SVM) is established based on the fruit ROI-HOG feature. First, the RGB image is transforme...

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Autores principales:	Jianping Ou, Jun Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/ff093d641b9b4879af5e3d347d2bbe99
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spelling	oai:doaj.org-article:ff093d641b9b4879af5e3d347d2bbe992021-11-15T01:19:10ZInvestigation on Recognition Performance of Harvesting Robot Using Regions of Interest Histogram of Oriented Gradients Feature Based on Improved Fuzzy Least Square Support Vector Machine1563-514710.1155/2021/6650367https://doaj.org/article/ff093d641b9b4879af5e3d347d2bbe992021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6650367https://doaj.org/toc/1563-5147In order to solve the problems such as big errors, lack of universality, and too much time consuming occurred in the recognition of overlapped fruits, an improved fuzzy least square support vector machine (FLS-SVM) is established based on the fruit ROI-HOG feature. First, the RGB image is transformed into saturation and value (HSV) image, and then the regions of interest (ROI) are detected from HSV color information. Finally, the histogram of oriented gradients (HOG) feature of ROI will be used as the input of FLS-SVM pattern recognizer to realize the recognition of picking fruit. In addition, the verified FLS-SVM is used to investigate the recognition performance of harvesting robot using regions of interest histogram of oriented gradients feature. The results reveal that the vector sizes are effectively reduced and a higher detection speed is achieved without compromising accuracy relative to conventional approaches. Similarly, the detection accuracy for the learning samples, the isolated fruit, the overlapped fruit, and the background can achieve 99.50%, 96.0%, 89.9%, and 97.0%, respectively, which shows the good performance of the proposed improved ROI-HOG feature recognition method.Jianping OuJun ZhangHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Jianping Ou Jun Zhang Investigation on Recognition Performance of Harvesting Robot Using Regions of Interest Histogram of Oriented Gradients Feature Based on Improved Fuzzy Least Square Support Vector Machine
description	In order to solve the problems such as big errors, lack of universality, and too much time consuming occurred in the recognition of overlapped fruits, an improved fuzzy least square support vector machine (FLS-SVM) is established based on the fruit ROI-HOG feature. First, the RGB image is transformed into saturation and value (HSV) image, and then the regions of interest (ROI) are detected from HSV color information. Finally, the histogram of oriented gradients (HOG) feature of ROI will be used as the input of FLS-SVM pattern recognizer to realize the recognition of picking fruit. In addition, the verified FLS-SVM is used to investigate the recognition performance of harvesting robot using regions of interest histogram of oriented gradients feature. The results reveal that the vector sizes are effectively reduced and a higher detection speed is achieved without compromising accuracy relative to conventional approaches. Similarly, the detection accuracy for the learning samples, the isolated fruit, the overlapped fruit, and the background can achieve 99.50%, 96.0%, 89.9%, and 97.0%, respectively, which shows the good performance of the proposed improved ROI-HOG feature recognition method.
format	article
author	Jianping Ou Jun Zhang
author_facet	Jianping Ou Jun Zhang
author_sort	Jianping Ou
title	Investigation on Recognition Performance of Harvesting Robot Using Regions of Interest Histogram of Oriented Gradients Feature Based on Improved Fuzzy Least Square Support Vector Machine
title_short	Investigation on Recognition Performance of Harvesting Robot Using Regions of Interest Histogram of Oriented Gradients Feature Based on Improved Fuzzy Least Square Support Vector Machine
title_full	Investigation on Recognition Performance of Harvesting Robot Using Regions of Interest Histogram of Oriented Gradients Feature Based on Improved Fuzzy Least Square Support Vector Machine
title_fullStr	Investigation on Recognition Performance of Harvesting Robot Using Regions of Interest Histogram of Oriented Gradients Feature Based on Improved Fuzzy Least Square Support Vector Machine
title_full_unstemmed	Investigation on Recognition Performance of Harvesting Robot Using Regions of Interest Histogram of Oriented Gradients Feature Based on Improved Fuzzy Least Square Support Vector Machine
title_sort	investigation on recognition performance of harvesting robot using regions of interest histogram of oriented gradients feature based on improved fuzzy least square support vector machine
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/ff093d641b9b4879af5e3d347d2bbe99
work_keys_str_mv	AT jianpingou investigationonrecognitionperformanceofharvestingrobotusingregionsofinteresthistogramoforientedgradientsfeaturebasedonimprovedfuzzyleastsquaresupportvectormachine AT junzhang investigationonrecognitionperformanceofharvestingrobotusingregionsofinteresthistogramoforientedgradientsfeaturebasedonimprovedfuzzyleastsquaresupportvectormachine
_version_	1718429023526191104

Investigation on Recognition Performance of Harvesting Robot Using Regions of Interest Histogram of Oriented Gradients Feature Based on Improved Fuzzy Least Square Support Vector Machine

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