scholarly journals Using YOLOv3 Algorithm with Pre- and Post-Processing for Apple Detection in Fruit-Harvesting Robot

Agronomy ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1016 ◽  
Author(s):  
Anna Kuznetsova ◽  
Tatiana Maleva ◽  
Vladimir Soloviev
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Error Rates ◽  
Machine Vision System ◽  
Detection Time ◽  
Post Processing ◽  
Processing Techniques ◽  
Harvesting Robot ◽  
A Share

A machine vision system for detecting apples in orchards was developed. The system was designed to be used in harvesting robots and is based on a YOLOv3 algorithm with special pre- and post-processing. The proposed pre- and post-processing techniques made it possible to adapt the YOLOv3 algorithm to be used in an apple-harvesting robot machine vision system, providing an average apple detection time of 19 ms with a share of objects being mistaken for apples at 7.8% and a share of unrecognized apples at 9.2%. Both the average detection time and error rates are less than in all known similar systems. The system can operate not only in apple-harvesting robots but also in orange-harvesting robots.

A Machine Vision System for Tomato Cluster Harvesting Robot

2009 ◽  
Vol 2 (2) ◽  
pp. 60-65 ◽  
Author(s):  
Naoshi Kondo ◽  
Kazuya Yamamoto ◽  
Hiroshi Shimizu ◽  
Koki Yata ◽  
Mitsutaka Kurita ◽  
...  
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Machine Vision System ◽  
Harvesting Robot

Multifunctional stereoscopic machine vision system with multiple 3D outputs

2020 ◽  
Keyword(s):  
Machine Vision ◽  
International Symposium ◽  
Fast Track ◽  
Vision System ◽  
Machine Vision System ◽  
Stereoscopic Displays ◽  
Electronic Imaging

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Stereoscopic Displays and Applications proceedings.

scholarly journals A real-time machine-vision system for monitoring the textile raising process

2005 ◽  
Vol 56 (8-9) ◽  
pp. 831-842 ◽  
Author(s):  
Monica Carfagni ◽  
Rocco Furferi ◽  
Lapo Governi
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Vision System ◽  
Machine Vision System ◽  
Time Machine

Application of Machine Vision System in the Precision Inspection

2012 ◽  
Vol 546-547 ◽  
pp. 1382-1386
Author(s):  
Yin Xia Liu ◽  
Ping Zhou
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Machine Vision System ◽  
Technical Problems ◽  
Application Program ◽  
Precision Inspection ◽  
Machine Parts

In order to promote the application and development of machine vision, The paper introduces the components of a machine vision system、common lighting technique and machine vision process. And the key technical problems are also briefly discussed in the application. A reference idea for application program of testing the quality of the machine parts is offered.

A machine vision system for on-line removal of contaminants in wool

Mechatronics ◽  
2006 ◽  
Vol 16 (5) ◽  
pp. 243-247 ◽  
Author(s):  
Zhenwei Su ◽  
Gui Yun Tian ◽  
Chunhua Gao
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Machine Vision System ◽  
On Line ◽  
Removal Of Contaminants

A novel method based on machine vision system and deep learning to detect fraud in turmeric powder

2021 ◽  
pp. 104728
Author(s):  
Ahmad Jahanbakhshi ◽  
Yousef Abbaspour-Gilandeh ◽  
Kobra Heidarbeigi ◽  
Mohammad Momeny
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Vision System ◽  
Machine Vision System ◽  
Novel Method

Research on Machine Vision in Forestry Pluck System

2010 ◽  
Vol 139-141 ◽  
pp. 2199-2202
Author(s):  
Xin Li ◽  
Chun Liang Zhang ◽  
Li Jun Li ◽  
Zhi Hu
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Area Ratio ◽  
Self Organization ◽  
Machine Vision System ◽  
Camellia Oleifera ◽  
Threshold Method ◽  
Input Feature ◽  
Ga Algorithm ◽  
Forestry Industry

Forestry industry is an important part of nation's economy. In this paper, a machine vision system is presented as a key module of Camellia oleifera pluck robot. In order to cut fruit image up from complicate background, SOFM neural network and gray thresh is used in image segmentation. In SOFM method, take R-B,G-R,G-B and hue H tunnel as input feature vectors, use self-organization network to clustering can get the best effect. in gray threshold method can take various of method to get the best threshold, such as PSO and GA algorithm, and MATLAB includes the toolboxes. At last use noise ratio, area ratio, divided time, Fourier boundary descriptors and other indicators to assess the accuracy of segmentation. The methods have the significance to the current and subsequent research of forestry pluck device.

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