The quiet revolution in machine vision - a state-of-the-art survey paper, including historical review, perspectives, and future directions

Smith, Melvyn L.; Smith, Lyndon; Hansen, Mark F.

doi:10.1016/j.compind.2021.103472

Cited by 111 publications

(59 citation statements)

References 22 publications

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“…3a). The light source should have a reasonable illumination, favorable homogeneity, and stability (Smith et al 2021), so that the measured object can be distinguished from the background as obviously as possible to obtain a high stability, high quality, and high contrast image (He et al 2020a). The camera lens is similar to the crystalline lens in the human eye.…”

Section: Composition and Function Of Machine Visionmentioning

confidence: 99%

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The applications of machine vision in raw material and production of wood products

Wang

Zhan²,

et al. 2022

BioRes

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Machine vision has been developed nearly for 70 years and been widely applied in electronics, automotive manufacturing, food processing, etc. With deepening study of its theory and technology in forestry industry, the industry of wood products is moving steadily toward the goal of automated identification and production to improve the manufacturing intelligence of enterprises. In this study, theoretical and algorithmic research on image acquisition, feature extraction, recognition, and classification involved in machine vision-based wood recognition technology were analyzed on the basis of its global development. The applications of machine vision in the wood materials, such as the identification of tree species, wood inspection and classification, defects detection of wood product, surface analysis of wood color, and quality control of furnishing products were thoroughly analyzed. The development trend of machine vision in the production and management of wood materials was considered in the current development of wood and furnishing enterprises. These results lay a solid foundation for wood science research, and intelligent manufacture of wooden furniture, and efficient development of greener and cleaner production of the furniture industry, which could improve the environmental effect of the wood products and furniture and make a great contribution for the carbon goal of “30-60” in China.

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Section: Composition and Function Of Machine Visionmentioning

confidence: 99%

“…1). The machine functions with human-like vision and understanding through "its own eyes" (Smith et al 2021). While vision begins with the eye, its true meaning is in the brain; seeing is not equal to understanding (Serre 2019).…”

Section: Introductionmentioning

confidence: 99%

The applications of machine vision in raw material and production of wood products

Wang

Zhan²,

et al. 2022

BioRes

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“…Smith et al [5] presents the study of machine vision including historical aspects, perspectives, and future enhancements. This work highlighted the importance of machine vision in manufacturing (e.g., quality inspection), security (e.g., biometric applications), medicine (e.g., detecting diseases), and in agriculture (e.g., plant disease detection).…”

Section: Related Workmentioning

confidence: 99%

“…Besides, with the improvement of production efficiency, the rate and accuracy of manual inspection are far from meeting the requirements [3]. In recent years, machine vision technology has developed rapidly and has shown outstanding performance in appearance detection [4,5]. Highspeed industrial quality inspection systems based on machine vision can integrate the functions of collecting target images, sending collected data, analyzing data, making judgments, executing instructions, etc., which can achieve a high degree of automation.…”

Section: Introductionmentioning

confidence: 99%

Design of Hardware Acceleration in Edge Computing Device for Bottle Cap High-Speed Inspection

Liu

Peng

et al. 2022

Wireless Communications and Mobile Computing

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High-speed product appearance inspection is crucial for modern automated industrial production such as bottle caps. The main detection method is to capture the image of bottle caps on the high-speed conveyor belt by industrial cameras and send them to the server through edge devices for various analyses. In order to improve production efficiency, it is necessary to increase the inspection rate of bottle caps. However, the transmission rate and huge data throughput of traditional inspection methods limit the inspection rate. Because of the application requirements for improving bottle cap detection efficiency, this paper proposes a hardware acceleration design based on edge devices to improve the bottle cap detection rate significantly. In this paper, an image processing module based on FPGA is designed as the edge device, improving algorithm execution speed through pipeline processing. It realizes the edge detection of the bottle cap and the fast detection of the front or back states and can send the instructions to the actuator to correct it in time when the back bottle cap is detected. It also realizes the positioning of the bottle cap area and cuts the image. Thereby, the amount of data sent to the server is significantly reduced. We have done both functional simulation and hardware implementation. Comparing with the pure software solution, the proposed design reduces the execution time of the algorithm from 16 ms to 3.07 ms, which achieves a more than four times rate increase. The amount of data that needs to be transmitted to the server per second is reduced from 7200 Kb is reduced to 25 Kb, which reduces the transmission capacity and server cache space by more than 280 times compared to the original image. In this paper, the hardware acceleration design of edge devices and the positioning and cropping of original images can greatly reduce the transmission pressure, data calculation pressure, and buffer space requirements of the central server and improve the detection rate. This design can not only be used for bottle cap inspection but also for various machine vision fields, such as defect detection of other workpieces.

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“…Automated solutions would need to explicitly consider these types of content. Algorithms exist for the identification of objects within images (see, e.g., [67]), detecting video manipulation (see, e.g., [68]) and transcription of speech into text (see, e.g., [69]). One solution for multimedia files would be to use these types of algorithms to supply summaries that could be evaluated along with the text of the page.…”

Section: Multimedia Contentmentioning

confidence: 99%

Protection from ‘Fake News’: The Need for Descriptive Factual Labeling for Online Content

2021

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So-called ‘fake news’—deceptive online content that attempts to manipulate readers—is a growing problem. A tool of intelligence agencies, scammers and marketers alike, it has been blamed for election interference, public confusion and other issues in the United States and beyond. This problem is made particularly pronounced as younger generations choose social media sources over journalistic sources for their information. This paper considers the prospective solution of providing consumers with ‘nutrition facts’-style information for online content. To this end, it reviews prior work in product labeling and considers several possible approaches and the arguments for and against such labels. Based on this analysis, a case is made for the need for a nutrition facts-based labeling scheme for online content.

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The quiet revolution in machine vision - a state-of-the-art survey paper, including historical review, perspectives, and future directions

Cited by 111 publications

References 22 publications

The applications of machine vision in raw material and production of wood products

The applications of machine vision in raw material and production of wood products

Design of Hardware Acceleration in Edge Computing Device for Bottle Cap High-Speed Inspection

Protection from ‘Fake News’: The Need for Descriptive Factual Labeling for Online Content

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