A Deep-Learning Based Solution to Automatically Control Closure and Seal of Pizza Packages

Banús, Núria; Boada, Imma; Bardera, Anton; Toldrà, Pol

doi:10.1109/access.2021.3135973

Cited by 10 publications

(4 citation statements)

References 49 publications

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“…Statistics key points scope (08) end for (09) Use a square to enclose the scope (10) if (the square is beyond the bounds of the image = false) (11) Increase the square by a factor of k (12) end if (13) Cut out the square to form a smaller picture (14) Save this picture (15) end ALGORITHM 1: Image cropping.…”

Section: Coarse-fine-grained Feature Fusion Architecturementioning

confidence: 99%

Automobile Component Recognition Based on Deep Learning Network with Coarse-Fine-Grained Feature Fusion

Tan

Xia

2023

International Journal of Intelligent Systems

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With the development of artificial intelligence, machine vision technology based on deep learning is an effective way to improve production efficiency. Because of the rapid update of the automobile manufacturing industry and the large variety of products, the learning time and the number of learning samples of the deep learning model are limited, which brings great difficulties to the recognition of components. Therefore, considering the economic benefits of enterprises, this paper proposes an intelligent component recognition method appropriate for small datasets, aiming to explore an automatic system for component recognition suitable for industrial manufacturing environments. The method completes the generation of the dataset through the system architecture with the potential for automation and the image cropping method based on feature detection and then designs a deep learning network based on coarse-fine-grained feature fusion to generate an intelligent recognition model of components. Finally, the designed network achieves an accuracy of 95.11%, and compared with the traditional classical network on multiple datasets, the designed network has better performance. Thus, the proposed method can improve the production flexibility of the automobile manufacturing industry and improve equipment intelligence.

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Section: Coarse-fine-grained Feature Fusion Architecturementioning

confidence: 99%

Automobile Component Recognition Based on Deep Learning Network with Coarse-Fine-Grained Feature Fusion

Tan

Xia

2023

International Journal of Intelligent Systems

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“…In addition, in addition to sealing tray contamination, another critical aspect of the packaging operation is quality control, where machine learning algorithms can automatically detect anomalies and reject non-compliant products on the production line. Banus et al [ 94 ] proposed a computer vision system based on the CNN algorithm to automate package sealing and tight sealing inspection to satisfy the production needs. In the study, pizza packages were experimented with for closure and sealing to ensure food storage conditions.…”

Section: Applications Of Machine Learning and Hsi In The Food Supply ...mentioning

confidence: 99%

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

et al. 2022

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Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.

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“…Although they were the first to achieve optimization of the operating parameters, the accuracy and reliability of the optimization were low. With the development of machine vision technology in recent years, vision technology has been widely used in many fields [3][4] [5] [6] , and it has also obtained certain applications in the feature extraction of mineral ore belt images [7] . The image processing algorithm of gray-scale calculus is used to calculate the boundary points of the ore belt, and the characteristics of the boundary line of the mineral ore belt are obtained by fitting, and finally the width and color characteristics of the ore belt are obtained, so as to realize the complete segmentation of the mineral ore belt [8] , with the help of these research, Beijing General Research Institute of Mining and Metallurgy(BGRMM) has developed an automatic mining system of gravity beneficiation Tabling [9] , which is mainly composed of a mining device and an inspection robot with a camera and image processing software, which improves the efficiency of mineral separation by the Tabling, instead of workers intercepting concentrates, and realizing automatic interception of concentrates on the gravity beneficiation Tabling [10] [11] .…”

Section: Introductionmentioning

confidence: 99%

Intelligent Deployment Solution for Tabling Adapting Deep Learning

You

Liu

2023

IEEE Access

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The deep learning object detection model can well extract the coordinate information of the separating point of the concentrate ore belt boundary line. However, with the drawbacks of acquired zonal image features, it is difficult to obtain the perfect prediction of the concentrate grade and recovery rate. In this study, by adapting deep learning semantic segmentation technique with DeepLab V3+, which can effectively extract multi-dimensional image features. The mapping relationship between image features and attributes of the Tabling equipment is achieved by constructing a multi-output support vector regression model optimized of a sparrow search algorithm (SSA-MSVR). The beneficiation indicators and operating parameters of the Tabling can be continually detected and optimized by an intelligent system that mainly includes image recognition softwares, automatic control units, data processing and communication workstations, and matching intelligent equipment, which accomplished the intelligent deployment solution of the Tabling beneficiation process.

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A Deep-Learning Based Solution to Automatically Control Closure and Seal of Pizza Packages

Cited by 10 publications

References 49 publications

Automobile Component Recognition Based on Deep Learning Network with Coarse-Fine-Grained Feature Fusion

Automobile Component Recognition Based on Deep Learning Network with Coarse-Fine-Grained Feature Fusion

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

Intelligent Deployment Solution for Tabling Adapting Deep Learning

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