scholarly journals Computer Vision Techniques in Manufacturing

2021 ◽  
Author(s):  
Longfei Zhou ◽  
Lin Zhang ◽  
Nicholas Konz
Keyword(s):  
Computer Vision ◽  
Product Life Cycle ◽  
Manufacturing Systems ◽  
Feature Detection ◽  
Rapid Development ◽  
Planning And Scheduling ◽  
System A ◽  
Control Assembly ◽  
Implementation Data ◽  
Algorithm Implementation

Computer vision techniques have played an important role in promoting the informatization, digitization and intelligence of industrial manufacturing systems. Considering the rapid development of computer vision techniques, we present a comprehensive review of the state-of-the-art of these techniques and their applications in manufacturing industries. We survey the most common methods, including feature detection, recognition, segmentation and 3D modeling. A system framework of computer vision in the manufacturing environment is proposed, consisting of a lighting module, a manufacturing system, a sensing module, computer vision algorithms, a decision-making module, and an actuator. Applications of computer vision to different stages of the entire product life cycle are then explored, including product design, modeling and simulation, planning and scheduling, the production process, inspection and quality control, assembly, transportation, and disassembly. Challenges include algorithm implementation, data pre-processing, data labeling, and benchmarks. Future directions include building benchmarks, developing methods for non-annotated data processing, developing effective data pre-processing mechanisms, customizing computer vision models, and opportunities aroused by 5G.

scholarly journals Computer Vision Techniques in Manufacturing

2021 ◽  
Author(s):  
Longfei Zhou ◽  
Lin Zhang ◽  
Nicholas Konz
Keyword(s):  
Computer Vision ◽  
Product Life Cycle ◽  
Manufacturing Systems ◽  
Feature Detection ◽  
Rapid Development ◽  
Planning And Scheduling ◽  
System A ◽  
Control Assembly ◽  
Implementation Data ◽  
Algorithm Implementation

Computer vision techniques have played an important role in promoting the informatization, digitization and intelligence of industrial manufacturing systems. Considering the rapid development of computer vision techniques, we present a comprehensive review of the state-of-the-art of these techniques and their applications in manufacturing industries. We survey the most common methods, including feature detection, recognition, segmentation and 3D modeling. A system framework of computer vision in the manufacturing environment is proposed, consisting of a lighting module, a manufacturing system, a sensing module, computer vision algorithms, a decision-making module, and an actuator. Applications of computer vision to different stages of the entire product life cycle are then explored, including product design, modeling and simulation, planning and scheduling, the production process, inspection and quality control, assembly, transportation, and disassembly. Challenges include algorithm implementation, data pre-processing, data labeling, and benchmarks. Future directions include building benchmarks, developing methods for non-annotated data processing, developing effective data pre-processing mechanisms, customizing computer vision models, and opportunities aroused by 5G.

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

Recognition of Birds in Blurred and Illumination Images by Local Features

2017 ◽  
Vol 7 (7) ◽  
pp. 243
Author(s):  
Suresha .M ◽  
. Sandeep
Keyword(s):  
Computer Vision ◽  
Time Complexity ◽  
Feature Detection ◽  
Feature Matching ◽  
Fast Algorithms ◽  
Local Features ◽  
Experimental Demonstration ◽  
Harris Corner ◽  
Blurred Image ◽  
Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware

2019 ◽  
Vol 31 (6) ◽  
pp. 844-850 ◽  
Author(s):  
Kevin T. Huang ◽  
Michael A. Silva ◽  
Alfred P. See ◽  
Kyle C. Wu ◽  
Troy Gallerani ◽  
...  
Keyword(s):  
Computer Vision ◽  
Feature Detection ◽  
High Accuracy ◽  
Detection Accuracy ◽  
Data Sets ◽  
Visual Words ◽  
Fusion Systems ◽  
Kaze Feature ◽  
Applications Of Machine Learning ◽  
Cervical Plating

OBJECTIVERecent advances in computer vision have revolutionized many aspects of society but have yet to find significant penetrance in neurosurgery. One proposed use for this technology is to aid in the identification of implanted spinal hardware. In revision operations, knowing the manufacturer and model of previously implanted fusion systems upfront can facilitate a faster and safer procedure, but this information is frequently unavailable or incomplete. The authors present one approach for the automated, high-accuracy classification of anterior cervical hardware fusion systems using computer vision.METHODSPatient records were searched for those who underwent anterior-posterior (AP) cervical radiography following anterior cervical discectomy and fusion (ACDF) at the authors’ institution over a 10-year period (2008–2018). These images were then cropped and windowed to include just the cervical plating system. Images were then labeled with the appropriate manufacturer and system according to the operative record. A computer vision classifier was then constructed using the bag-of-visual-words technique and KAZE feature detection. Accuracy and validity were tested using an 80%/20% training/testing pseudorandom split over 100 iterations.RESULTSA total of 321 total images were isolated containing 9 different ACDF systems from 5 different companies. The correct system was identified as the top choice in 91.5% ± 3.8% of the cases and one of the top 2 or 3 choices in 97.1% ± 2.0% and 98.4 ± 13% of the cases, respectively. Performance persisted despite the inclusion of variable sizes of hardware (i.e., 1-level, 2-level, and 3-level plates). Stratification by the size of hardware did not improve performance.CONCLUSIONSA computer vision algorithm was trained to classify at least 9 different types of anterior cervical fusion systems using relatively sparse data sets and was demonstrated to perform with high accuracy. This represents one of many potential clinical applications of machine learning and computer vision in neurosurgical practice.

scholarly journals Measurement of Gem Colour Using a Computer Vision System: A Case Study with Jadeite-Jade

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 791
Author(s):  
Sufei Zhang ◽  
Ying Guo
Keyword(s):  
Computer Vision ◽  
Regression Model ◽  
Vision System ◽  
Expert Group ◽  
Group Test ◽  
System A ◽  
Real Objects ◽  
Practical Tool ◽  
Almost All

This paper introduces computer vision systems (CVSs), which provides a new method to measure gem colour, and compares CVS and colourimeter (CM) measurements of jadeite-jade colour in the CIELAB space. The feasibility of using CVS for jadeite-jade colour measurement was verified by an expert group test and a reasonable regression model in an experiment involving 111 samples covering almost all jadeite-jade colours. In the expert group test, more than 93.33% of CVS images are considered to have high similarities with real objects. Comparing L*, a*, b*, C*, h, and ∆E* (greater than 10) from CVS and CM tests indicate that significant visual differences exist between the measured colours. For a*, b*, and h, the R2 of the regression model for CVS and CM was 90.2% or more. CVS readings can be used to predict the colour value measured by CM, which means that CVS technology can become a practical tool to detect the colour of jadeite-jade.

scholarly journals Open Source Control Device for Industry 4.0 Based on RAMI 4.0

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 869
Author(s):  
Pablo F. S. Melo ◽  
Eduardo P. Godoy ◽  
Paolo Ferrari ◽  
Emiliano Sisinni
Keyword(s):  
Open Source ◽  
Industry 4.0 ◽  
Product Life Cycle ◽  
Manufacturing Systems ◽  
Industrial Revolution ◽  
Control Device ◽  
Source Control ◽  
Reference Architecture ◽  
Architecture Model ◽  
Complex Relationships

The technical innovation of the fourth industrial revolution (Industry 4.0—I4.0) is based on the following respective conditions: horizontal and vertical integration of manufacturing systems, decentralization of computing resources and continuous digital engineering throughout the product life cycle. The reference architecture model for Industry 4.0 (RAMI 4.0) is a common model for systematizing, structuring and mapping the complex relationships and functionalities required in I4.0 applications. Despite its adoption in I4.0 projects, RAMI 4.0 is an abstract model, not an implementation guide, which hinders its current adoption and full deployment. As a result, many papers have recently studied the interactions required among the elements distributed along the three axes of RAMI 4.0 to develop a solution compatible with the model. This paper investigates RAMI 4.0 and describes our proposal for the development of an open-source control device for I4.0 applications. The control device is one of the elements in the hierarchy-level axis of RAMI 4.0. Its main contribution is the integration of open-source solutions of hardware, software, communication and programming, covering the relationships among three layers of RAMI 4.0 (assets, integration and communication). The implementation of a proof of concept of the control device is discussed. Experiments in an I4.0 scenario were used to validate the operation of the control device and demonstrated its effectiveness and robustness without interruption, failure or communication problems during the experiments.

scholarly journals A Review of Engineering Research in Sustainable Manufacturing

2013 ◽  
Vol 135 (4) ◽  
Author(s):  
Karl R. Haapala ◽  
Fu Zhao ◽  
Jaime Camelio ◽  
John W. Sutherland ◽  
Steven J. Skerlos ◽  
...  
Keyword(s):  
Decision Making ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Sustainable Manufacturing ◽  
Supply Chain Design ◽  
Planning And Scheduling ◽  
Engineering Research ◽  
Challenges And Opportunities ◽  
Recent Developments ◽  
Planning Development

Sustainable manufacturing requires simultaneous consideration of economic, environmental, and social implications associated with the production and delivery of goods. Fundamentally, sustainable manufacturing relies on descriptive metrics, advanced decision-making, and public policy for implementation, evaluation, and feedback. In this paper, recent research into concepts, methods, and tools for sustainable manufacturing is explored. At the manufacturing process level, engineering research has addressed issues related to planning, development, analysis, and improvement of processes. At a manufacturing systems level, engineering research has addressed challenges relating to facility operation, production planning and scheduling, and supply chain design. Though economically vital, manufacturing processes and systems have retained the negative image of being inefficient, polluting, and dangerous. Industrial and academic researchers are re-imagining manufacturing as a source of innovation to meet society's future needs by undertaking strategic activities focused on sustainable processes and systems. Despite recent developments in decision making and process- and systems-level research, many challenges and opportunities remain. Several of these challenges relevant to manufacturing process and system research, development, implementation, and education are highlighted.

A Blockchain-Enabled Approach for Online Stream Sensor Data Protection in Cyber-Physical Manufacturing Systems

2021 ◽  
Author(s):  
Zhangyue Shi ◽  
Chenang Liu ◽  
Chen Kan ◽  
Wenmeng Tian ◽  
Yang Chen
Keyword(s):  
Information Technologies ◽  
Manufacturing Systems ◽  
Rapid Development ◽  
Data Access ◽  
Sensor Data ◽  
False Alarms ◽  
Monitoring And Control ◽  
Stream Data ◽  
Physical Security ◽  
And Control

Abstract With the rapid development of the Internet of Things and information technologies, more and more manufacturing systems become cyber-enabled, which significantly improves the flexibility and productivity of manufacturing. Furthermore, a large variety of online sensors are also commonly incorporated in the manufacturing systems for online quality monitoring and control. However, the cyber-enabled environment may pose the collected online stream sensor data under high risks of cyber-physical attacks as well. Specifically, cyber-physical attacks could occur during the manufacturing process to maliciously tamper the sensor data, which could result in false alarms or failures of anomaly detection. In addition, the cyber-physical attacks may also illegally access the collected data without authorization and cause leakage of key information. Therefore, it becomes critical to develop an effective approach to protect online stream data from these attacks so that the cyber-physical security of the manufacturing systems could be assured. To achieve this goal, an integrative blockchain-enabled method, is proposed by leveraging both asymmetry encryption and camouflage techniques. A real-world case study that protects cyber-physical security of collected stream data in additive manufacturing is provided to demonstrate the effectiveness of the proposed method. The results demonstrate that malicious tampering could be detected in a relatively short time and the risk of unauthorized data access is significantly reduced as well.

Sign in / Sign up

Export Citation Format

Share Document