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 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.

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Human Detection Based on SVM and Improved Histogram of Oriented Gradients

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.380-384.3862 ◽

2013 ◽

Vol 380-384 ◽

pp. 3862-3865 ◽

Cited By ~ 1

Author(s):

Li Hong Zhang

Keyword(s):

Support Vector Machine ◽

Linear Function ◽

Detection System ◽

Features Extraction ◽

The Body ◽

Human Detection ◽

Support Vector ◽

Detection Accuracy ◽

Histogram Of Oriented Gradients ◽

Feature Description

Considering the fact that original histogram of oriented gradients (HOG) cannot extract the body local features in large image regions, its features are improved when extracted, then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows. Considering speed, we select support vector machine (SVM) using linear function kernel as a classifier. Combining with HOG extraction and SVM training, the process includes three steps: features extraction, training and detection. Experiments show that while maintaining a relatively satisfactory speed the human detection system improves detection accuracy.

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Continuous wide spectrum odor sensing for electronic nose system

Sensor Review ◽

10.1108/sr-04-2017-0067 ◽

2018 ◽

Vol 38 (2) ◽

pp. 223-230

Author(s):

Wenli Zhang ◽

Fengchun Tian ◽

An Song ◽

Zhenzhen Zhao ◽

Youwen Hu ◽

...

Keyword(s):

Support Vector Machine ◽

Nearest Neighbor ◽

Wide Spectrum ◽

Least Square ◽

Support Vector ◽

Detection Accuracy ◽

K Nearest Neighbor ◽

Sensing Element ◽

Content Type ◽

System Errors

Purpose This paper aims to propose an odor sensing system based on wide spectrum for e-nose, based on comprehensive analysis on the merits and drawbacks of current e-nose. Design/methodology/approach The wide spectral light is used as the sensing medium in the e-nose system based on continuous wide spectrum (CWS) odor sensing, and the sensing response of each sensing element is the change of light intensity distribution. Findings Experimental results not only verify the feasibility and effectiveness of the proposed system but also show the effectiveness of least square support vector machine (LSSVM) in eliminating system errors. Practical implications Theoretical model of the system was constructed, and experimental tests were carried out by using NO2 and SO2. System errors in the test data were eliminated using the LSSVM, and the preprocessed data were classified by euclidean distance to centroids (EDC), k-nearest neighbor (KNN), support vector machine (SVM), LSSVM, respectively. Originality/value The system not only has the advantages of current e-nose but also realizes expansion of sensing array by means of light source and the spectrometer with their wide spectrum, high resolution characteristics which improve the detection accuracy and realize real-time detection.

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