A Low-Cost Embedded Computer Vision System for the Classification of Recyclable Objects

2021 ◽  
pp. 11-30
Author(s):  
Karl Myers ◽  
Emanuele Lindo Secco
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Low Cost ◽  
Computer Vision System ◽  
Embedded Computer Vision ◽  
Embedded Computer

scholarly journals An Embedded Computer Vision System for Beans Quality Inspection

2020 ◽  
Vol 175 (24) ◽  
pp. 44-53
Author(s):  
Robson A.G. Macedo ◽  
Peterson A. Belan ◽  
Sidnei A. Araújo
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Quality Inspection ◽  
Computer Vision System ◽  
Embedded Computer Vision ◽  
Embedded Computer

An Embedded Computer-Vision System for Multi-Object Detection in Traffic Surveillance

2019 ◽  
Vol 20 (11) ◽  
pp. 4006-4018 ◽  
Author(s):  
Ala Mhalla ◽  
Thierry Chateau ◽  
Sami Gazzah ◽  
Najoua Essoukri Ben Amara
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Vision System ◽  
Traffic Surveillance ◽  
Computer Vision System ◽  
Embedded Computer Vision ◽  
Embedded Computer

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

scholarly journals Study and classification of the Crystallographic Orientation Distribution Function of a non-grain oriented electrical steel using computer vision system

2019 ◽  
Vol 8 (1) ◽  
pp. 1070-1083
Author(s):  
Roberto Fernandes Ivo ◽  
Douglas de Araújo Rodrigues ◽  
José Ciro dos Santos ◽  
Francisco Nélio Costa Freitas ◽  
Luis Flaávio Gaspar Herculano ◽  
...  
Keyword(s):  
Computer Vision ◽  
Distribution Function ◽  
Orientation Distribution Function ◽  
Crystallographic Orientation ◽  
Electrical Steel ◽  
Vision System ◽  
Orientation Distribution ◽  
Computer Vision System

A computer vision system for the detection and classification of vehicles at urban road intersections

2005 ◽  
Vol 8 (1-2) ◽  
pp. 17-31 ◽  
Author(s):  
Stefano Messelodi ◽  
Carla Maria Modena ◽  
Michele Zanin
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Urban Road ◽  
Road Intersections
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