Fast object detection in pastoral landscapes using a Colour Feature Extreme Learning Machine

2017 ◽  
Vol 139 ◽  
pp. 204-212 ◽  
Author(s):  
Edmund J. Sadgrove ◽  
Greg Falzon ◽  
David Miron ◽  
David Lamb
Keyword(s):  
Object Detection ◽  
Extreme Learning Machine ◽  
Pastoral Landscapes ◽  
Colour Feature ◽  
Learning Machine

Data and feature mixed ensemble based extreme learning machine for medical object detection and segmentation

2015 ◽  
Vol 75 (5) ◽  
pp. 2815-2837 ◽  
Author(s):  
Wanzheng Zhu ◽  
Weimin Huang ◽  
Zhiping Lin ◽  
Yongzhong Yang ◽  
Su Huang ◽  
...  
Keyword(s):  
Object Detection ◽  
Extreme Learning Machine ◽  
Medical Object ◽  
Learning Machine ◽  
Mixed Ensemble

The Segmented Colour Feature Extreme Learning Machine: Applications in Agricultural Robotics

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2290
Author(s):  
Edmund J. Sadgrove ◽  
Greg Falzon ◽  
David Miron ◽  
David W. Lamb
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Model Performance ◽  
Image Features ◽  
Majority Voting ◽  
Decision Matrix ◽  
Colour Image ◽  
Colour Feature ◽  
Learning Machine ◽  
Performance Results

This study presents the Segmented Colour Feature Extreme Learning Machine (SCF-ELM). The SCF-ELM is inspired by the Extreme Learning Machine (ELM) which is known for its rapid training and inference times. The ELM is therefore an ideal candidate for an ensemble learning algorithm. The Colour Feature Extreme Learning Machine (CF-ELM) is used in this study due to its additional ability to extract colour image features. The SCF-ELM is an ensemble learner that utilizes feature mapping via k-means clustering, a decision matrix and majority voting. It has been evaluated on a range of challenging agricultural object classification scenarios including weed, livestock and machinery detection. SCF-ELM model performance results were excellent both in terms of detection, 90 to 99% accuracy, and also inference times, around 0.01(s) per image. The SCF-ELM was able to compete or improve upon established algorithms in its class, indicating its potential for remote computing applications in agriculture.

Sign in / Sign up

Export Citation Format

Share Document