Autonomous Recognition System for Barcode Detection in Complex Scenes

Chen, Chen; He, Bingwei; Zhang, Liwei; Yan, Pingkun

doi:10.1051/itmconf/20171204016

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Other classical methods rely on a geometrical line or bar detector [12][13][14][15]. In the case of two-dimensional codes, which are not the subject of this work, the gradient criterion is not sufficient, so edge detection is replaced by corner detection [9].…”

Section: Barcode Detection Summarymentioning

confidence: 99%

An Encoder–Decoder Architecture within a Classical Signal-Processing Framework for Real-Time Barcode Segmentation

Gómez-Cárdenes

Marichal-Hernández

Son

et al. 2023

Sensors

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In this work, two methods are proposed for solving the problem of one-dimensional barcode segmentation in images, with an emphasis on augmented reality (AR) applications. These methods take the partial discrete Radon transform as a building block. The first proposed method uses overlapping tiles for obtaining good angle precision while maintaining good spatial precision. The second one uses an encoder–decoder structure inspired by state-of-the-art convolutional neural networks for segmentation while maintaining a classical processing framework, thus not requiring training. It is shown that the second method’s processing time is lower than the video acquisition time with a 1024 × 1024 input on a CPU, which had not been previously achieved. The accuracy it obtained on datasets widely used by the scientific community was almost on par with that obtained using the most-recent state-of-the-art methods using deep learning. Beyond the challenges of those datasets, the method proposed is particularly well suited to image sequences taken with short exposure and exhibiting motion blur and lens blur, which are expected in a real-world AR scenario. Two implementations of the proposed methods are made available to the scientific community: one for easy prototyping and one optimised for parallel implementation, which can be run on desktop and mobile phone CPUs.

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Section: Barcode Detection Summarymentioning

confidence: 99%

An Encoder–Decoder Architecture within a Classical Signal-Processing Framework for Real-Time Barcode Segmentation

Gómez-Cárdenes

Marichal-Hernández

Son

et al. 2023

Sensors

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“…Barcodes are the most common of the AIDCs used in the last 5 decades [4,5] . Barcodes are simply the machine-readable vertical black strips with white spaces which are printed and found on most products [6][7][8] . Advancement in barcode technology has led to two-dimensional (2D) barcodes being developed [5,9,10] .…”

Section: Introduction mentioning

confidence: 99%

“…In recent years deep learning and artificial intelligence with Hough transform and morphological operations is increasingly being used to localize and decode barcodes [5,[29][30][31] . The Parallel Line Segment Detector with Hough transform and morphological operations have also been used to decode barcodes in real-time [7,32] . Furthermore, the Zamberletti algorithm has recently been used to detect multiple 1D and 2D barcode images [8] .…”

Section: Introduction mentioning

confidence: 99%

Comparison of manual and automatic barcode detection in rough horticultural production systems

Eyahanyo

Rath

2019

International Journal of Agricultural and Biological Engineering

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Automation of production in the nurseries of flower producing companies using barcode scanners have been attempted but with little success. Stationary laser barcode scanners which have been used for automation have failed due to the close proximity between the barcode and the scanner, and factors such as speed, angle of inclination of the barcode, damage to the barcode and dirt on the barcode. Furthermore, laser barcode scanners are still being used manually in the nurseries making work laborious and time consuming, which leading to reduced productivity. Therefore, an automated image-based barcode detection system to help solve the aforementioned problems was proposed. Experiments were conducted under different situations with clean and artificially soiled Code 128 barcodes in both the laboratory and under real production conditions in a flower producing company. The images were analyzed with a specific algorithm developed with the software tool Halcon. Overall the results from the company showed that the image-based system has a future prospect for automation in the nursery.

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An algorithm of the recognition of inferior barcode

Chuanliang¹,

Xia²,

Lv³

et al. 2020

J. Phys.: Conf. Ser.

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Objective: Bar code recognition is widely used. This paper introduces a method of using digital image algorithm to complete the identification of stain bar code. Methods: firstly, the barcode image was binarized and positioned; secondly, the pixels in the barcode image were stacked on the abscissa axis by projection technology to remove the barcode stains; finally, the barcodes were effectively identified by the center distance algorithm. Results: the experiments show that the barcodes with different degrees of pollution are well recognized by our method, which is better than the traditional recognition algorithm. Conclusion: This paper proposes a new barcode recognition algorithm, which can quickly and accurately identify the bad barcode

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Autonomous Recognition System for Barcode Detection in Complex Scenes

Cited by 4 publications

References 7 publications

An Encoder–Decoder Architecture within a Classical Signal-Processing Framework for Real-Time Barcode Segmentation

An Encoder–Decoder Architecture within a Classical Signal-Processing Framework for Real-Time Barcode Segmentation

Comparison of manual and automatic barcode detection in rough horticultural production systems

An algorithm of the recognition of inferior barcode

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