Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks
Product quality control is currently the leading trend in industrial production. It is heading towards the exact analysis of each product before reaching the end customer. Every stage of production control is of particular importance in the food and pharmaceutical industries, where, apart from visua...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/2/501 |
| _version_ | 1797412574391697408 |
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| author | Marcin Malesa Piotr Rajkiewicz |
| author_facet | Marcin Malesa Piotr Rajkiewicz |
| author_sort | Marcin Malesa |
| collection | DOAJ |
| description | Product quality control is currently the leading trend in industrial production. It is heading towards the exact analysis of each product before reaching the end customer. Every stage of production control is of particular importance in the food and pharmaceutical industries, where, apart from visual issues, additional safety regulations are demanded. Many production processes can be controlled completely contactless through the use of machine vision cameras and advanced image processing techniques. The most dynamically growing sector of image analysis methods are solutions based on deep neural networks. Their major advantages are fast performance, robustness, and the fact that they can be exploited even in complicated classification problems. However, the use of machine learning methods on high-performance production lines may be limited by inference time or, in the case of multiformated production lines, training time. The article presents a novel data preprocessing (or calibration) method. It uses prior knowledge about the optical system, which enables the use of the lightweight Convolutional Neural Network (CNN) model for product quality control of polyethylene terephthalate (PET) bottle caps. The combination of preprocessing with the lightweight CNN model resulted in at least a five-fold reduction in prediction and training time compared to the lighter standard models tested on ImageNet, without loss of accuracy. |
| first_indexed | 2024-03-09T05:04:05Z |
| format | Article |
| id | doaj.art-791ed587bb0843cea15479ae8e267d4c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:04:05Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-791ed587bb0843cea15479ae8e267d4c2023-12-03T12:56:30ZengMDPI AGSensors1424-82202021-01-0121250110.3390/s21020501Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural NetworksMarcin Malesa0Piotr Rajkiewicz1KSM Vision sp. z o.o., ul. Sokołowska 9/117, 01-142 Warsaw, PolandKSM Vision sp. z o.o., ul. Sokołowska 9/117, 01-142 Warsaw, PolandProduct quality control is currently the leading trend in industrial production. It is heading towards the exact analysis of each product before reaching the end customer. Every stage of production control is of particular importance in the food and pharmaceutical industries, where, apart from visual issues, additional safety regulations are demanded. Many production processes can be controlled completely contactless through the use of machine vision cameras and advanced image processing techniques. The most dynamically growing sector of image analysis methods are solutions based on deep neural networks. Their major advantages are fast performance, robustness, and the fact that they can be exploited even in complicated classification problems. However, the use of machine learning methods on high-performance production lines may be limited by inference time or, in the case of multiformated production lines, training time. The article presents a novel data preprocessing (or calibration) method. It uses prior knowledge about the optical system, which enables the use of the lightweight Convolutional Neural Network (CNN) model for product quality control of polyethylene terephthalate (PET) bottle caps. The combination of preprocessing with the lightweight CNN model resulted in at least a five-fold reduction in prediction and training time compared to the lighter standard models tested on ImageNet, without loss of accuracy.https://www.mdpi.com/1424-8220/21/2/501machine visionquality controlneural networksimage processing |
| spellingShingle | Marcin Malesa Piotr Rajkiewicz Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks Sensors machine vision quality control neural networks image processing |
| title | Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks |
| title_full | Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks |
| title_fullStr | Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks |
| title_full_unstemmed | Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks |
| title_short | Quality Control of PET Bottles Caps with Dedicated Image Calibration and Deep Neural Networks |
| title_sort | quality control of pet bottles caps with dedicated image calibration and deep neural networks |
| topic | machine vision quality control neural networks image processing |
| url | https://www.mdpi.com/1424-8220/21/2/501 |
| work_keys_str_mv | AT marcinmalesa qualitycontrolofpetbottlescapswithdedicatedimagecalibrationanddeepneuralnetworks AT piotrrajkiewicz qualitycontrolofpetbottlescapswithdedicatedimagecalibrationanddeepneuralnetworks |