Efficient visual code localization with neural networks

Bodnár, Péter; Grósz, Tamás; Tóth, László; Nyúl, László G.

doi:10.1007/s10044-017-0619-6

Cited by 11 publications

(10 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is observed that the bounding-box output by the model does not fully contain the barcode region, and so additional processing steps are introduced to refine the accuracy of the proposed region. Meanwhile, [17] divides an input image into small patches and uses a fully connected network to make a binary decision as to whether a code exists within a given patch. As such, the neural network is not used in an end-toend manner, and the framework is very task-specific due to the hand-crafted feature extraction process.…”

Section: A Scene Text Detection and Barcode Detectionmentioning

confidence: 99%

Coarse Annotation Refinement for Segmentation of Dot-Matrix Batchcodes

Jia

Holder

Bonner

et al. 2019

2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA)

View full text Add to dashboard Cite

Deep Convolutional Neural Networks (CNN) have been extensively applied in various computer vision tasks. Although such approaches have demonstrated exceptionally high performance in various open challenges, adapting them to more specialised tasks can be non-trivial. In this paper we discuss our design and implementation of a batchcode detection system capable of accurate segmentation of batchcode regions within images of consumer products. A batchcode is a unique identifier printed on the packaging of many products that encodes useful information such as date and location of manufacture. Detection of batchcodes in images of products is a useful step in many processes, including quality control, supply chain tracking and counterfeit detection. Beginning with a unique dataset of product images and a set of crowdsourced coarse annotations that roughly correspond to the locations of batchcodes, we demonstrate that such annotations are insufficient for training a reliable model, and subsequently describe a novel label refinement process, which we call the Maximally Stable Global Region (MSGR) method, that we use to generate accurate ground-truth data suitable for training a robust neural network. We also show that detection accuracy can be further improved by applying MSGR to the output of the neural network. We evaluate our approach using a manually labelled test dataset of images of shampoo bottles, and demonstrate the efficacy of the proposed method for accurate real-time batchcode detection.

show abstract

Section: A Scene Text Detection and Barcode Detectionmentioning

confidence: 99%

Coarse Annotation Refinement for Segmentation of Dot-Matrix Batchcodes

Jia

Holder

Bonner

et al. 2019

2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA)

View full text Add to dashboard Cite

show abstract

“…Based on the probability matrix, the QR code was localized. Further, this method was extended to barcode localization [16] and got a good result. A two-step algorithm [17] was proposed to localize the QR code.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for DPM Code Localization Based on Depthwise Separable Convolution

Tian

et al. 2019

IEEE Access

View full text Add to dashboard Cite

With the popular application of direct part mark (DPM) technology, DPM code inspection has been a hot issue in the machine vision. It mainly consists of two steps, namely, localization and decoding. DPM code localization is a key and complex step in the DPM code inspection. However, the traditional localization methods suffer from complex imaging environment, involving various imaging background, illumination, imaging distance, and exposures. Furthermore, the target itself, i.e., the DPM code, could be severely polluted or worn. Aiming at improving the performance and robustness of DPM code localization, an efficient method with depthwise separable convolution is proposed in this paper. The optimized network model has the advantages of a few parameters, high computational efficiency, high precision localization, and good generalization ability. Meanwhile, the precision of the DPM code region is improved with the help of multi-scale prediction. The experiments on our DPM code localization database demonstrate the effectiveness and flexibility of the proposed method in comparison with the YOLOv3 network and the Tiny_YOLO network. Furthermore, the proposed method can estimate the exposure level of the DPM code region, which is benefiting to the DPM code recognition and enables the adaptive ability.INDEX TERMS Direct part mark, data matrix, depthwise separable convolution, deep learning.

show abstract

“…Automatic Identification Data Capture (AIDC) such as barcodes, Radio Frequency Identification (RFID), smart cards etc., have been developed to replace manual data collection and to provide an accurate, quick, and efficient means of capturing and storing data [1][2][3] . 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] .…”

Section: Introduction mentioning

confidence: 99%

“…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] . Barcodes have been used extensively in horticultural production systems to eliminate the laborious and time-consuming process of manual data entry and to capture information about plants and products to which they are attached [6,11] .…”

Section: Introduction mentioning

confidence: 99%

“…However, these methods have been used mostly in combination with each other. 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] .…”

Section: Introduction mentioning

confidence: 99%

See 1 more Smart Citation

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

Eyahanyo

Rath

2019

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

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.

show abstract

Efficient visual code localization with neural networks

Cited by 11 publications

References 19 publications

Coarse Annotation Refinement for Segmentation of Dot-Matrix Batchcodes

Coarse Annotation Refinement for Segmentation of Dot-Matrix Batchcodes

An Efficient Method for DPM Code Localization Based on Depthwise Separable Convolution

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

Contact Info

Product

Resources

About