Automated Quality Control of Cleaning Processes in Automotive Components Using Blob Analysis
This study presents an automated computer vision system for assessing the cleanliness of plastic mirror caps used in the automotive industry after a washing process. These components are highly visible and require optimal surface conditions prior to painting, making the detection of residual contami...
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| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/9/2710 |
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| author | Simone Mari Giovanni Bucci Fabrizio Ciancetta Edoardo Fiorucci Andrea Fioravanti |
| author_facet | Simone Mari Giovanni Bucci Fabrizio Ciancetta Edoardo Fiorucci Andrea Fioravanti |
| author_sort | Simone Mari |
| collection | DOAJ |
| description | This study presents an automated computer vision system for assessing the cleanliness of plastic mirror caps used in the automotive industry after a washing process. These components are highly visible and require optimal surface conditions prior to painting, making the detection of residual contaminants critical for quality assurance. The system acquires high-resolution monochrome images under various lighting configurations, including natural light and infrared (IR) at 850 nm and 940 nm, with different angles of incidence. Four blob detection algorithms—adaptive thresholding, Laplacian of Gaussian (LoG), Difference of Gaussians (DoG), and Determinant of Hessian (DoH)—were implemented and evaluated based on their ability to detect surface impurities. Performance was assessed by comparing the total detected blob area before and after the cleaning process, providing a proxy for both sensitivity and false positive rate. Among the tested methods, adaptive thresholding under 30° natural light produced the best results, with a statistically significant z-score of +2.05 in the pre-wash phase and reduced false detections in post-wash conditions. The LoG and DoG methods were more prone to spurious detections, while DoH demonstrated intermediate performance but struggled with reflective surfaces. The proposed approach offers a cost-effective and scalable solution for real-time quality control in industrial environments, with the potential to improve process reliability and reduce waste due to surface defects. |
| format | Article |
| id | doaj-art-04063e922a6342eca67d54ddf49c8ac1 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-04063e922a6342eca67d54ddf49c8ac12025-08-20T02:58:44ZengMDPI AGSensors1424-82202025-04-01259271010.3390/s25092710Automated Quality Control of Cleaning Processes in Automotive Components Using Blob AnalysisSimone Mari0Giovanni Bucci1Fabrizio Ciancetta2Edoardo Fiorucci3Andrea Fioravanti4Dipartimento di Ingegneria Industriale e Dell’informazione e di Economia, Università dell’Aquila, 67100 L’Aquila, ItalyDipartimento di Ingegneria Industriale e Dell’informazione e di Economia, Università dell’Aquila, 67100 L’Aquila, ItalyDipartimento di Ingegneria Industriale e Dell’informazione e di Economia, Università dell’Aquila, 67100 L’Aquila, ItalyDipartimento di Ingegneria Industriale e Dell’informazione e di Economia, Università dell’Aquila, 67100 L’Aquila, ItalyDipartimento di Ingegneria Industriale e Dell’informazione e di Economia, Università dell’Aquila, 67100 L’Aquila, ItalyThis study presents an automated computer vision system for assessing the cleanliness of plastic mirror caps used in the automotive industry after a washing process. These components are highly visible and require optimal surface conditions prior to painting, making the detection of residual contaminants critical for quality assurance. The system acquires high-resolution monochrome images under various lighting configurations, including natural light and infrared (IR) at 850 nm and 940 nm, with different angles of incidence. Four blob detection algorithms—adaptive thresholding, Laplacian of Gaussian (LoG), Difference of Gaussians (DoG), and Determinant of Hessian (DoH)—were implemented and evaluated based on their ability to detect surface impurities. Performance was assessed by comparing the total detected blob area before and after the cleaning process, providing a proxy for both sensitivity and false positive rate. Among the tested methods, adaptive thresholding under 30° natural light produced the best results, with a statistically significant z-score of +2.05 in the pre-wash phase and reduced false detections in post-wash conditions. The LoG and DoG methods were more prone to spurious detections, while DoH demonstrated intermediate performance but struggled with reflective surfaces. The proposed approach offers a cost-effective and scalable solution for real-time quality control in industrial environments, with the potential to improve process reliability and reduce waste due to surface defects.https://www.mdpi.com/1424-8220/25/9/2710automotive manufacturingblob analysiscomputer visionquality controlsurface inspection |
| spellingShingle | Simone Mari Giovanni Bucci Fabrizio Ciancetta Edoardo Fiorucci Andrea Fioravanti Automated Quality Control of Cleaning Processes in Automotive Components Using Blob Analysis Sensors automotive manufacturing blob analysis computer vision quality control surface inspection |
| title | Automated Quality Control of Cleaning Processes in Automotive Components Using Blob Analysis |
| title_full | Automated Quality Control of Cleaning Processes in Automotive Components Using Blob Analysis |
| title_fullStr | Automated Quality Control of Cleaning Processes in Automotive Components Using Blob Analysis |
| title_full_unstemmed | Automated Quality Control of Cleaning Processes in Automotive Components Using Blob Analysis |
| title_short | Automated Quality Control of Cleaning Processes in Automotive Components Using Blob Analysis |
| title_sort | automated quality control of cleaning processes in automotive components using blob analysis |
| topic | automotive manufacturing blob analysis computer vision quality control surface inspection |
| url | https://www.mdpi.com/1424-8220/25/9/2710 |
| work_keys_str_mv | AT simonemari automatedqualitycontrolofcleaningprocessesinautomotivecomponentsusingblobanalysis AT giovannibucci automatedqualitycontrolofcleaningprocessesinautomotivecomponentsusingblobanalysis AT fabriziociancetta automatedqualitycontrolofcleaningprocessesinautomotivecomponentsusingblobanalysis AT edoardofiorucci automatedqualitycontrolofcleaningprocessesinautomotivecomponentsusingblobanalysis AT andreafioravanti automatedqualitycontrolofcleaningprocessesinautomotivecomponentsusingblobanalysis |