scholarly journals Fast recognition method for citrus under complex environments based on improved YOLOv3

2021 ◽  
Author(s):  
Xu Xiao ◽  
Jingjing Huang ◽  
Ming Li ◽  
Yongwei Xu ◽  
Hongduo Zhang ◽  
...  
Keyword(s):  
Complex Environments ◽  
Recognition Method ◽  
Fast Recognition

scholarly journals Fast Recognition Method of Football Robot’s Graphics From the VR Perspective

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 161472-161479
Author(s):  
Zhen Bai ◽  
Liang Wang ◽  
Sheng Zhou ◽  
Yuan Cao ◽  
Ying Liu ◽  
...  
Keyword(s):  
Recognition Method ◽  
Fast Recognition

A fast recognition method of warhead target in boost phase using kinematic features

2015 ◽  
Author(s):  
Jian Chen ◽  
Shiyou Xu ◽  
Biao Tian ◽  
Jianhua Wu ◽  
Zengping Chen
Keyword(s):  
Recognition Method ◽  
Fast Recognition

A 3-Dimensional Object Recognition Method Using Relationship of Distances and Angles in Feature Points

2017 ◽  
Vol 17 (04) ◽  
pp. 1750019 ◽  
Author(s):  
Seiichi Maehara ◽  
Kazuo Ikeshiro ◽  
Hiroki Imamura
Keyword(s):  
Object Recognition ◽  
Recognition Task ◽  
Feature Points ◽  
Recognition Method ◽  
3 Dimensional ◽  
Fast Recognition ◽  
Dimensional Object ◽  
Relationship Of ◽  
Human Support ◽  
Spin Image

In recent years, human support robots have been receiving attention. Especially, object recognition task is important in case that people request the robots to transport and rearrange an object. We consider that there are four necessary properties to recognize in domestic environment as follows. (1) Robustness against occlusion. (2) Fast recognition. (3) Pose estimation with high accuracy. (4) Coping with erroneous correspondences. As conventional object recognition methods using 3-dimensional information, there are model-based recognition methods such as the SHOT and the Spin Image. The SHOT and the Spin Image do not satisfy all four properties for the robots. Therefore, to satisfy the four properties of recognition, we propose a 3-dimensional object recognition method by using relationship of distances and angles in feature points. As per our approach, the proposed method achieves to solve problems of conventional methods by using not only the feature points but also relationship between feature points. To achieve this purpose, firstly, the proposed method uses a curvature as a feature in a local region. Secondly, the proposed method uses points having high curvature as feature points. Finally, the proposed method generates a list by listing relationship of distances and angles between feature points and matches lists.

scholarly journals A Rapid Recognition Method for Electronic Components Based on the Improved YOLO-V3 Network

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 825 ◽  
Author(s):  
Rui Huang ◽  
Jinan Gu ◽  
Xiaohong Sun ◽  
Yongtao Hou ◽  
Saad Uddin
Keyword(s):  
Deep Learning ◽  
Intelligent Manufacturing ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Electronic Components ◽  
Recognition Method ◽  
Image Labeling ◽  
Fast Recognition ◽  
Speed And Accuracy ◽  
Detection Speed

Rapid object recognition in the industrial field is the key to intelligent manufacturing. The research on fast recognition methods based on deep learning was the focus of researchers in recent years, but the balance between detection speed and accuracy was not well solved. In this paper, a fast recognition method for electronic components in a complex background is presented. Firstly, we built the image dataset, including image acquisition, image augmentation, and image labeling. Secondly, a fast recognition method based on deep learning was proposed. The balance between detection accuracy and detection speed was solved through the lightweight improvement of YOLO (You Only Look Once)-V3 network model. Finally, the experiment was completed, and the proposed method was compared with several popular detection methods. The results showed that the accuracy reached 95.21% and the speed was 0.0794 s, which proved the superiority of this method for electronic component detection.

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