A New Machine Vision Method for Target Detection and Localization of Malleable Iron Pipes: An Experimental Case

Pan, Zhengqiang; Zhang, Dong

doi:10.5755/j02.eie.33004

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…In the process of acquisition of the image of the tobacco shred, there will be impurities such as the residue of the tobacco shred, and some points or areas will be dark in binary processing (Fig. 3) [14]- [17]. These factors will affect the subsequent tobacco shred count.…”

Section: B Remove Small Target In Binary Imagesmentioning

confidence: 99%

Machine Vision Method for Quantitative Statistics Analysis of Industrial Product Images

Zan,

Hu,

Jin

et al. 2023

ELEKTRON ELEKTROTECH

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To address the problems of unstable accuracy, low efficiency, and subjective influence of manual counting, a machine vision-based method to count the quantity of tobacco shreds is proposed for the first time. In this paper, the complex tobacco shred image is obtained by backlight imaging. The adaptive threshold segmentation method is used to segment tobacco shreds. The pixel area of the tobacco shred area is calculated by connected domain labelling. Second, independent tobacco shreds and adhesive tobacco shreds were identified based on the pixel area, and the quantity of segmented tobacco shreds was counted for the first time. Subsequently, in complex scenarios (such as tobacco shreds adhesive and overlapping), an image is usually obtained by manually drawing the contours of the adhesive and overlapping tobacco shreds on the basis of primary statistics. Finally, different individuals are distinguished, segmentation is completed, and tobacco shred quantity statistics are realised. The experimental results show that the average accuracy is 100.0 % for quantitative statistics of independent tobacco shred images. For tobacco shred images with adhesive and overlapping interference, the minimum accuracy is 90 %, and the accuracy increases with the increase in tobacco shred quantity. Furthermore, the efficiency of the tobacco shred quantity statistics conducted by the method in this paper was only affected by complex scenarios. Compared to artificial processing, the efficiency was increased by more than 100 %. The work in this paper can provide the technical basis for measuring the dimensions of tobacco shreds.

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Section: B Remove Small Target In Binary Imagesmentioning

confidence: 99%

Machine Vision Method for Quantitative Statistics Analysis of Industrial Product Images

Zan,

Hu,

Jin

et al. 2023

ELEKTRON ELEKTROTECH

View full text Add to dashboard Cite

show abstract

“…For example, improved grey wolf (IGW) with a new dimension learning-based hunting (DLH) search strategy [21]. However, to our knowledge, the selection of RF, the IGW-optimised ANN surrogate model, and the MOP based on IGW have not been adequately integrated for cigarette machines [22]- [27]. It is worth investigating the parameter optimisation of a cigarette machine swarm using these mentioned techniques [28]- [30], especially for using hybrid intelligent techniques [31]- [34].…”

Section: Introductionmentioning

confidence: 99%

Operation Parameters Optimisation of a Machine Swarm Using Artificial Intelligence

Zhong,

Rao,

Zhang

et al. 2023

ELEKTRON ELEKTROTECH

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Due to improper setting of operating parameters, cigarette machines are subject to a high unqualified production rate. For this reason, in this study, a multiobjective optimisation (MOP) method based on the metaheuristic intelligence optimisation is proposed in this study. First, to eliminate interference parameters, the random forest (RF) is used to analyse the parameter importance of the cigarette machine and select the most important operation parameters for the multiobjective optimisation. Second, an artificial neural network (ANN) optimised by the grey wolf optimiser is designed to establish a mirror model of the cigarette machine to fast calculate the machine output quality factors, including the rod break rate, single cigarette weight, and circumference index. Lastly, an improved multiobjective grey wolf optimisation algorithm is used to optimise these three quality factors simultaneously to obtain the optimal operating parameters of the cigarette machine. A machine swarm (including four cigarette machines) in the real world is used to evaluate the developed optimisation method, and the testing results demonstrate that the proposed multiobjective optimisation method is able to improve the three quality factors by at least 50 %, which greatly reduces the unqualified rate of cigarettes.

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A New Machine Vision Method for Target Detection and Localization of Malleable Iron Pipes: An Experimental Case

Cited by 2 publications

References 19 publications

Machine Vision Method for Quantitative Statistics Analysis of Industrial Product Images

Machine Vision Method for Quantitative Statistics Analysis of Industrial Product Images

Operation Parameters Optimisation of a Machine Swarm Using Artificial Intelligence

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