System of detection and scanning bar codes from Raspberry Pi web camera

Diachok, Roman; Dunets, Roman; Klym, Halyna

doi:10.1109/dessert.2018.8409124

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…In many cases cameras with interchangeable lenses are used, such as the increasingly popular Raspberry Pi camera used in conventional applications such as [1,2], and even pushed into more specialised applications such as [3]. Lenses are typically the most expensive parts of the imaging hardware, it is thus logical that in a low-cost application usage of cheap lenses is desirable.…”

Section: Introductionmentioning

confidence: 99%

On Bayer demosaicing impact on image sharpness in lens quality assessment

Zemčík¹,

Horák²

2022

Proceedings II of the 28st Conference STUDENT EEICT 2022: Selected Papers.

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In this short paper an ongoing research is introduced into the methods of simple lens quality assessment for low-cost computer vision applications. Also the paradigm of colour filter arrays (CFA), CFA images and their demosaicing are described. Further the paper introduces an experiment to compare selected demosaicing algorithms and their influences on image sharpness, and therefore on the lens quality assessment. Of the selected methods the AHD (Adaptive Homogeneity-Directed) algorithm proved the most suitable for the lens evaluation.

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Section: Introductionmentioning

confidence: 99%

On Bayer demosaicing impact on image sharpness in lens quality assessment

Zemčík¹,

Horák²

2022

Proceedings II of the 28st Conference STUDENT EEICT 2022: Selected Papers.

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Vehicle image segmentation using mobile-based K-mean clustering to determine tax costs in supporting regional basic income

Yuliawati¹,

Kurniawan²,

Nugroho³

et al. 2023

International Conference on Applied Computational Intelligence and Analytics (Acia-2022)

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Label Design and Extraction in High-Temperature Logistics Based on Concave Coding and MLFFA-DeepLabV3+ Network

Zhao

Yin³

et al. 2023

JACIII

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Logistics tracking technology at normal temperature is quite mature, but there are few tracking methods for the high-temperature production process. The main difficulties are that the label materials generally used cannot withstand the high temperature for a long time, and the detection devices are vulnerable to environmental impact. A high-temperature logistics tracking solution was developed for a carbon anode used in an aluminum electrolysis factory. It is based on concave coding and a multiscale low-level feature fusion and attention-DeepLabV3+ (MLFFA-DeepLabV3+) network extraction technique for the coded region of the concave coding. The concave coding is printed on the product as a tag that can endure a high temperature of more than 1,200°C, ensuring its integrity and identifiability. Because there is no obvious color distinction between the coding area and the background, direct recognition is ineffective. The MLFFA-DeepLabV3+ network extracts the coding region to improve the recognition rate. The DeepLabV3+ network is improved by replacing the backbone network and adding of a multiscale low-level feature fusion module and convolutional block attention module. Experimental results showed that the mean pixel accuracy and mean intersection over union of the MLFFA-DeepLabV3+ network increased by 2.37% and 2.45%, respectively, compared with the original DeepLabV3+ network. The network structure has only 11.24% of the number of parameters in the original structure. The solution is feasible and provides a basis for high-temperature logistics tracking technology in intelligent manufacturing.

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System of detection and scanning bar codes from Raspberry Pi web camera

Cited by 3 publications

References 11 publications

On Bayer demosaicing impact on image sharpness in lens quality assessment

On Bayer demosaicing impact on image sharpness in lens quality assessment

Vehicle image segmentation using mobile-based K-mean clustering to determine tax costs in supporting regional basic income

Label Design and Extraction in High-Temperature Logistics Based on Concave Coding and MLFFA-DeepLabV3+ Network

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