Detection and Recognition of Characters on the Surface of Metal Workpieces with Complex Background

Li, Jing; Huang, Tao; Yang, Yilei; Xu, Qi

doi:10.1109/itnec48623.2020.9085200

Cited by 8 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, for a single task, there are two options for execution: (1) the task runs on the edge device; (2) the data is uploaded to the cloud, and then runs in the cloud, that is the so-called task offloading. 13 Let's set the image detection applications (e.g., product surface defect detection, 14 printed circuit board defect detection, 15 metal surface engraving character detection, 16 etc.) based on computer vision in smart factories as an example.…”

Section: Introductionmentioning

confidence: 99%

Container‐based data‐intensive application scheduling in hybrid cloud‐edge collaborative environment

2023

View full text Add to dashboard Cite

Container virtualization technology represented by Docker has been widely used in the industry due to its advantages of lightweight, fast deployment, and easy portability. It can bring convenience to system deployment, operation, and maintenance. This article considers the implementation of AI‐based IoT applications based on Docker and container technology in a cloud‐edge collaborative environment. Three typical data‐intensive applications, preprocessing, training, and inference in machine learning, are deployed using Docker containers. For these three types of tasks, this article proposes a container‐based data‐intensive application scheduling framework. Using an attribute‐based data‐driven method, a new scheduling approach considering multiple weighting factors is proposed, which is called DICS‐OPT. The cloud nodes and edge nodes are scored and sorted uniformly. The container is scheduled to the node with the highest score, and then the task data is transmitted to the node to execute the task. NSGA‐III‐based genetic algorithm has been proposed to search for the optimal weighting factors. This article introduces the implementation of the prototype system and builds a cloud‐edge collaborative testbed consisting of Raspberry Pis and PCs. The performance evaluation results indicates that the proposed scheduling approach outperforms existing approaches. Compared with existing approaches, DICS‐OPT improves the average edge resource utilization by 10.05% to 69.04%, and saves the cloud resource cost by 16.02% to 36.68%.

show abstract

Section: Introductionmentioning

confidence: 99%

Container‐based data‐intensive application scheduling in hybrid cloud‐edge collaborative environment

2023

View full text Add to dashboard Cite

show abstract

“…If one wants to prevent any undesirable results, they can modify the visual realism of the image. By taking a close look at the image and making the necessary changes to brightness, hue, or contrast, one can achieve the desired result [7]. Taking this approach will guarantee that the image appears as natural as it can and any undesirable results are avoided.…”

Section: Introductionmentioning

confidence: 99%

“…Filtering techniques can be employed to heighten the quality of digital images, thus diminishing the amount of image noise [7][8][9]. Various strategies like blurring, smoothing, and sharpening are utilized in these approaches to decrease the quantity of unnecessary noise in the picture.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Hough Transform for Rectifying Industrial Nameplate Images: A Novel Strategy for Improved Text Detection and Precision in Difficult Environments

Li¹,

Ma²,

Bao³

et al. 2023

Preprint

View full text Add to dashboard Cite

Industrial nameplates serve as a means of conveying critical information and parameters. In this work, we propose a novel approach for rectifying industrial nameplate pictures utilizing a probabilistic Hough transform. Our method effectively corrects for distortions and clipping, and features a collection of challenging nameplate pictures for analysis. To determine the corners of the nameplate, we employ a progressive probability Hough transform, which not only enhances detection accuracy but also possesses the ability to handle complex industrial scenarios. The results of our approach are clear and readable nameplate text, as demonstrated through experiments that show improved accuracy in model identification compared to other methods.

show abstract

“…Under back-lighting and front-lighting environments, they used global and local threshold methods to achieve simultaneous segmentation of multiple metal parts. Li et al [19] proposed a method for detecting and recognizing surface features of metal workpieces. Aiming at the problems of uneven illumination and reflection on the surface of metal workpieces, the Retinex Currently, in the industrial field, machine vision [9] technology has been widely applied to image segmentation of metal workpieces to better improve industrial production efficiency [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Under back-lighting and front-lighting environments, they used global and local threshold methods to achieve simultaneous segmentation of multiple metal parts. Li et al [ 19 ] proposed a method for detecting and recognizing surface features of metal workpieces. Aiming at the problems of uneven illumination and reflection on the surface of metal workpieces, the Retinex algorithm was used for image enhancement, the Otus algorithm was used for the segmentation of target features on the surface of metal workpieces, and the vertical projection of a binary graph was used for the single separation of target features.…”

Section: Introductionmentioning

confidence: 99%

Magnesium Ingot Stacking Segmentation Algorithm for Industrial Robot Based on the Correction of Image Overexposure Area

Li,

Zheng,

Wang

et al. 2023

Sensors

View full text Add to dashboard Cite

This paper proposes an adaptive threshold segmentation algorithm for the magnesium ingot stack based on image overexposure area correction (ATSIOAC), which solves the problem of mirror reflection on the surface of magnesium alloy ingots caused by external ambient light and auxiliary light sources. Firstly, considering the brightness and chromaticity information of the mapped image, we divide the exposure probability threshold into weak exposure and strong exposure regions. Secondly, the saturation difference between the magnesium ingot region and the background region is used to obtain a mask for the magnesium ingot region to eliminate interference from the image background. Then, the RGB average of adjacent pixels in the overexposed area is used as a reference to correct the colors of the strongly exposed and weakly exposed areas, respectively. Furthermore, in order to smoothly fuse the two corrected images, pixel weighted average (WA) is applied. Finally, the magnesium ingot sorting experimental device was constructed and the corrected top surface image of the ingot pile was segmented through ATSIOAC. The experimental results show that the overexposed area detection and correction algorithm proposed in this paper can effectively correct the color information in the overexposed area, and when segmenting ingot images, complete segmentation results of the top surface of the ingot pile can be obtained, effectively improving the accuracy of magnesium alloy ingot segmentation. The segmentation algorithm achieves a segmentation accuracy of 94.38%.

show abstract

Detection and Recognition of Characters on the Surface of Metal Workpieces with Complex Background

Cited by 8 publications

References 3 publications

Container‐based data‐intensive application scheduling in hybrid cloud‐edge collaborative environment

Container‐based data‐intensive application scheduling in hybrid cloud‐edge collaborative environment

Probabilistic Hough Transform for Rectifying Industrial Nameplate Images: A Novel Strategy for Improved Text Detection and Precision in Difficult Environments

Magnesium Ingot Stacking Segmentation Algorithm for Industrial Robot Based on the Correction of Image Overexposure Area

Contact Info

Product

Resources

About