Araştırma Makalesi
BibTex RIS Kaynak Göster

Gerçek Zamanlı Kusur Tespiti: LPG Tüplerinin Yüzeylerinde Kirlilikleri Tanımlama için Görüntü İşleme ve Makine Öğrenimi Teknikleri ile Yenilikçi Bir Yaklaşım

Yıl 2024, Cilt: 24 Sayı: 2, 330 - 340, 29.04.2024
https://doi.org/10.35414/akufemubid.1364153

Öz

Kusur tespiti yapan sistemlerin tasarımındaki en büyük zorluklardan biri tasarlanan sistemin ne tür kusurlar üzerinde kullanılacağı ve hangi görüntü işleme yöntemlerini kullanacağı belirsizliğidir. Çizik çeşitleri, farklı türde yüzey aşınmaları ve gerçek zamanlı işleme ihtiyacı görüntü işleme ile yapılan kusur tespitinin önündeki zorlayıcı etmenlerden bazılarıdır. Tipik olarak, kusur analizi problemlerini ele almak için istatistiksel, spektral ve model tabanlı yaklaşımlar kullanılabilir. Model tabanlı tekniklerin güçlü bir alt kümesi olan makine öğrenimi, kusur analizinde giderek daha popüler hale gelmiştir. Bu çalışmada LPG dolum tesislerinde kullanılan LPG tüplerinin yüzeylerinde meydana gelen bozulmaların ve kusurların tespit edilmesi amaçlanmıştır. Meydana gelen bozulmalar tüplerin okunabilirliğini azalttığı gibi tüp okuma işlemlerinin doğruluğunu da azaltmaktadır bu sebeple bu bozulmaların tespit edilmesi sistemin doğruluğu açısından büyük önem taşımaktadır. Gerçek zamanlı çalışma hızına sahip olması amacıyla görüntü işleme ve makine öğrenmesi algoritmaları kullanılan yöntem bu yönüyle literatürdeki diğer çalışmalardan ayrılmaktadır. Yöntem, fabrika ortamında oluşturulan veri seti üzerinde uygulanmıştır. Çalışmamızda, temiz olarak tanımlanan LPG tüpleri için kirlilik oranı 2%'den düşük bulunmuştur ve bu tüplerin doğruluk değerlerinin standart sapması ortalama 0.27'dir. Buna karşılık, kirli olarak tanımlanan tüplerde ortalama kirlilik oranı 18% olarak tespit edilmiş ve bu tüplerin standart sapması 2.03 olarak hesaplanmıştır.

Kaynakça

  • Ashour M.W., Khalid F., Abdul Halin A., Abdullah L.N. and Darwish S.H., 2019. Surface defects classification of hot-rolled steel strips using multi-directional shearlet features. Arabian Journal for Science and Engineering, 44, 2925-2932. https://doi.org/10.1007/s13369-018-3329-5
  • Ayed I.B., Hennane N. and Mitiche A., 2006. Unsupervised variational image segmentation/classification using a Weibull observation model. IEEE transactions on Image processing, 15(11), 3431-3439. https://doi.org/10.1109/TIP.2006.881961
  • Bay H., Tuytelaars T., Van Gool L., 2006. SURF: Speeded Up Robust Features. Computer Vision – ECCV 2006, 404–417. https://doi.org/10.1007/11744023_32
  • Bhatt P.M., Malhan R.K., Rajendran P., Shah B.C., Thakar S., Yoon Y.J., Gupta S.K., 2021. Image-Based Surface Defect Detection Using Deep Learning: A Review. Journal of Computing and Information Science in Engineering, 21, 040801. https://doi.org/10.1115/1.4049535
  • Božič J., Tabernik D., Skočaj D., 2021. Mixed supervision for surface-defect detection: from weakly to fully supervised learning. Computers in Industry, 129, 103459. https://doi.org/10.1016/j.compind.2021.103459
  • Cha Y.J., Choi W. and Büyüköztürk O., 2017. Deep learning‐based crack damage detection using convolutional neural networks. Computer‐Aided Civil and Infrastructure Engineering, 32(5), 361-378. http://dx.doi.org/10.1111/mice.12263
  • Dong H., Song K., He Y., Xu J., Yan Y. and Meng Q., 2019. PGA-Net: Pyramid feature fusion and global context attention network for automated surface defect detection. IEEE Transactions on Industrial Informatics, 16(12), 7448-7458. https://doi.org/10.1109/TII.2019.2958826
  • Ester M., Kriegel H.P., Sander J., Xu X., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd, 96, 226–231.
  • Gayubo F., Gonzalez J.L., de la Fuente E., Miguel F. and Perán J.R., 2006. On-line machine vision system for detect split defects in sheet-metal forming processes. In 18th International Conference on Pattern Recognition (ICPR'06), 1, 723-726. https://doi.org/10.1109/ICPR.2006.902
  • Karayiannis Y.A., Stojanovic R., Mitropoulos P., Koulamas C., Stouraitis T., Koubias S., Papadopoulos G., 1999. Defect detection and classification on web textile fabric using multiresolution decomposition and neural networks. ICECS’99. Proceedings of ICECS ’99. 6th IEEE International Conference on Electronics, Circuits and Systems, 2, 765–768. https://doi.org/10.1109/ICECS.1999.813221
  • Karimi M.H., Asemani D., 2014. Surface defect detection in tiling Industries using digital image processing methods: Analysis and evaluation. ISA Transactions, 53, 834–844. https://doi.org/10.1016/j.isatra.2013.11.015
  • Kumar A., 2008. Computer-Vision-Based Fabric Defect Detection: A Survey. IEEE Transactions on Industrial Electronics, 55, 348–363. http://dx.doi.org/10.1109/TIE.1930.896476
  • Le X., Mei J., Zhang H., Zhou B., Xi J., 2020. A learning-based approach for surface defect detection using small image datasets. Neurocomputing, 408, 112–120. http://dx.doi.org/10.1016/j.neucom.2019.09.107
  • Li X., Wang C., Ju H., Li Z., 2022. Surface Defect Detection Model for Aero-Engine Components Based on Improved YOLOv5. Applied Sciences, 12, 7235. https://doi.org/10.3390/app12147235
  • Liu K., Wang H., Chen H., Qu E., Tian Y. and Sun H., 2017. Steel surface defect detection using a new Haar–Weibull-variance model in unsupervised manner. IEEE transactions on instrumentation and measurement, 66(10), 2585-2596. https://doi.org/10.1109/TIM.2017.2712838
  • Lu Q., Lin J., Luo L., Zhang Y., Zhu W., 2022. A supervised approach for automated surface defect detection in ceramic tile quality control. Advanced Engineering Informatics, 53, 101692. https://doi.org/10.1016/j.aei.2022.101692
  • Luo Q., Fang X., Liu L., Yang C., Sun Y., 2020. Automated Visual Defect Detection for Flat Steel Surface: A Survey. IEEE Transactions on Instrumentation and Measurement, 69(3), 626-644. https://doi.org/10.1109/TIM.2019.2963555
  • Park J.K., Kwon B.K., Park J.H. and Kang D.J., 2016. Machine learning-based imaging system for surface defect inspection. International Journal of Precision Engineering and Manufacturing-Green Technology, 3, 303-310. https://doi.org/10.1007/s40684-016-0039-x
  • Sakhare K., Kulkarni M., Kumbhakarn M., Kare N., 2015. Spectral and spatial domain approach for fabric defect detection and classification. 2015 International Conference on Industrial Instrumentation and Control (ICIC), Pune, India, 640-644. http://dx.doi.org/10.1109/IIC.2015.7150820
  • Sharifzadeh M., Amirfattahi R., Sadri S., Alirezaee S. and Ahmadi M., 2008. Detection of steel defect using the image processing algorithms. In The International Conference on Electrical Engineering ICEENG 2008, 1-7. https://doi.org/10.21608/iceeng.2008.34372
  • Tabernik D., Šela S., Skvarč J., Skočaj D., 2020. Segmentation-based deep-learning approach for surface-defect detection. Journal of Intelligent Manufacturing, 31, 759–776. https://doi.org/10.1007/s10845-019-01476-x
  • Uzen H., Turkoglu M., Hanbay D., 2023. Multi-dimensional feature extraction-based deep encoder–decoder network for automatic surface defect detection. Neural Computing and Applications, 35, 3263–3282. https://doi.org/10.1007/s00521-022-07885-z
  • Wang Z., Zhu H., Jia X., Bao Y., Wang C., 2022. Surface Defect Detection with Modified Real-Time Detector YOLOv3. Journal of Sensors, 2022, e8668149. https://doi.org/10.1155/2022/8668149
  • Weimer D., Scholz-Reiter B. and Shpitalni M., 2016. Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP annals, 65(1), 417-420. https://doi.org/10.1016/j.cirp.2016.04.072
  • Wu G., Kwak H., Jang S., Xu K. and Xu J., 2008. Design of online surface inspection system of hot rolled strips. In 2008 IEEE International Conference on Automation and Logistics, 2291-2295. https://doi.org/10.1109/ICAL.2008.4636548
  • Xiao L., Wu B., Hu Y., 2020. Surface Defect Detection Using Image Pyramid. IEEE Sensors Journal, 20, 7181–7188. https://doi.org/10.1109/JSEN.2020.2977366
  • Xing J., Jia M., 2021. A convolutional neural network-based method for workpiece surface defect detection. Measurement, 176, 109185. http://dx.doi.org/10.1016/j.measurement.2021.109185

Real-Time Defect Detection: An Innovative Approach to Identifying Surface Impurities on LPG Cylinders Using Image Processing and Machine Learning Techniques

Yıl 2024, Cilt: 24 Sayı: 2, 330 - 340, 29.04.2024
https://doi.org/10.35414/akufemubid.1364153

Öz

One of the primary challenges in designing defect detection systems lies in the uncertainty surrounding the types of defects the system will address and the image processing methods to be employed. Challenges such as variations in scratches, different kinds of surface wear, and the requirement for real-time processing often complicate defect detection via image processing. Typically, defect analysis can be approached using statistical, spectral, or model-based methods. Among these, machine learning a robust subset of the model-based techniques has gained significant traction in defect analysis. This study aims to identify deteriorations and defects on the surfaces of LPG cylinders used at LPG filling stations. Such deteriorations not only diminish the readability of these cylinders but also adversely affect the accuracy of cylinder reading operations. Consequently, detecting these imperfections is crucial for maintaining system accuracy. Uniquely, our method incorporates both image processing and machine learning algorithms to achieve real-time operational speeds, setting it apart from other literature. This methodology was tested on a dataset generated in an industrial setting. In our study, the dirtiness rate for LPG cylinders identified as clean was below 2%, and the average standard deviation of accuracy values for these cylinders was 0.27. In contrast, the average dirtiness rate for cylinders identified as dirty was 18%, with a higher standard deviation of 2.03.

Kaynakça

  • Ashour M.W., Khalid F., Abdul Halin A., Abdullah L.N. and Darwish S.H., 2019. Surface defects classification of hot-rolled steel strips using multi-directional shearlet features. Arabian Journal for Science and Engineering, 44, 2925-2932. https://doi.org/10.1007/s13369-018-3329-5
  • Ayed I.B., Hennane N. and Mitiche A., 2006. Unsupervised variational image segmentation/classification using a Weibull observation model. IEEE transactions on Image processing, 15(11), 3431-3439. https://doi.org/10.1109/TIP.2006.881961
  • Bay H., Tuytelaars T., Van Gool L., 2006. SURF: Speeded Up Robust Features. Computer Vision – ECCV 2006, 404–417. https://doi.org/10.1007/11744023_32
  • Bhatt P.M., Malhan R.K., Rajendran P., Shah B.C., Thakar S., Yoon Y.J., Gupta S.K., 2021. Image-Based Surface Defect Detection Using Deep Learning: A Review. Journal of Computing and Information Science in Engineering, 21, 040801. https://doi.org/10.1115/1.4049535
  • Božič J., Tabernik D., Skočaj D., 2021. Mixed supervision for surface-defect detection: from weakly to fully supervised learning. Computers in Industry, 129, 103459. https://doi.org/10.1016/j.compind.2021.103459
  • Cha Y.J., Choi W. and Büyüköztürk O., 2017. Deep learning‐based crack damage detection using convolutional neural networks. Computer‐Aided Civil and Infrastructure Engineering, 32(5), 361-378. http://dx.doi.org/10.1111/mice.12263
  • Dong H., Song K., He Y., Xu J., Yan Y. and Meng Q., 2019. PGA-Net: Pyramid feature fusion and global context attention network for automated surface defect detection. IEEE Transactions on Industrial Informatics, 16(12), 7448-7458. https://doi.org/10.1109/TII.2019.2958826
  • Ester M., Kriegel H.P., Sander J., Xu X., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd, 96, 226–231.
  • Gayubo F., Gonzalez J.L., de la Fuente E., Miguel F. and Perán J.R., 2006. On-line machine vision system for detect split defects in sheet-metal forming processes. In 18th International Conference on Pattern Recognition (ICPR'06), 1, 723-726. https://doi.org/10.1109/ICPR.2006.902
  • Karayiannis Y.A., Stojanovic R., Mitropoulos P., Koulamas C., Stouraitis T., Koubias S., Papadopoulos G., 1999. Defect detection and classification on web textile fabric using multiresolution decomposition and neural networks. ICECS’99. Proceedings of ICECS ’99. 6th IEEE International Conference on Electronics, Circuits and Systems, 2, 765–768. https://doi.org/10.1109/ICECS.1999.813221
  • Karimi M.H., Asemani D., 2014. Surface defect detection in tiling Industries using digital image processing methods: Analysis and evaluation. ISA Transactions, 53, 834–844. https://doi.org/10.1016/j.isatra.2013.11.015
  • Kumar A., 2008. Computer-Vision-Based Fabric Defect Detection: A Survey. IEEE Transactions on Industrial Electronics, 55, 348–363. http://dx.doi.org/10.1109/TIE.1930.896476
  • Le X., Mei J., Zhang H., Zhou B., Xi J., 2020. A learning-based approach for surface defect detection using small image datasets. Neurocomputing, 408, 112–120. http://dx.doi.org/10.1016/j.neucom.2019.09.107
  • Li X., Wang C., Ju H., Li Z., 2022. Surface Defect Detection Model for Aero-Engine Components Based on Improved YOLOv5. Applied Sciences, 12, 7235. https://doi.org/10.3390/app12147235
  • Liu K., Wang H., Chen H., Qu E., Tian Y. and Sun H., 2017. Steel surface defect detection using a new Haar–Weibull-variance model in unsupervised manner. IEEE transactions on instrumentation and measurement, 66(10), 2585-2596. https://doi.org/10.1109/TIM.2017.2712838
  • Lu Q., Lin J., Luo L., Zhang Y., Zhu W., 2022. A supervised approach for automated surface defect detection in ceramic tile quality control. Advanced Engineering Informatics, 53, 101692. https://doi.org/10.1016/j.aei.2022.101692
  • Luo Q., Fang X., Liu L., Yang C., Sun Y., 2020. Automated Visual Defect Detection for Flat Steel Surface: A Survey. IEEE Transactions on Instrumentation and Measurement, 69(3), 626-644. https://doi.org/10.1109/TIM.2019.2963555
  • Park J.K., Kwon B.K., Park J.H. and Kang D.J., 2016. Machine learning-based imaging system for surface defect inspection. International Journal of Precision Engineering and Manufacturing-Green Technology, 3, 303-310. https://doi.org/10.1007/s40684-016-0039-x
  • Sakhare K., Kulkarni M., Kumbhakarn M., Kare N., 2015. Spectral and spatial domain approach for fabric defect detection and classification. 2015 International Conference on Industrial Instrumentation and Control (ICIC), Pune, India, 640-644. http://dx.doi.org/10.1109/IIC.2015.7150820
  • Sharifzadeh M., Amirfattahi R., Sadri S., Alirezaee S. and Ahmadi M., 2008. Detection of steel defect using the image processing algorithms. In The International Conference on Electrical Engineering ICEENG 2008, 1-7. https://doi.org/10.21608/iceeng.2008.34372
  • Tabernik D., Šela S., Skvarč J., Skočaj D., 2020. Segmentation-based deep-learning approach for surface-defect detection. Journal of Intelligent Manufacturing, 31, 759–776. https://doi.org/10.1007/s10845-019-01476-x
  • Uzen H., Turkoglu M., Hanbay D., 2023. Multi-dimensional feature extraction-based deep encoder–decoder network for automatic surface defect detection. Neural Computing and Applications, 35, 3263–3282. https://doi.org/10.1007/s00521-022-07885-z
  • Wang Z., Zhu H., Jia X., Bao Y., Wang C., 2022. Surface Defect Detection with Modified Real-Time Detector YOLOv3. Journal of Sensors, 2022, e8668149. https://doi.org/10.1155/2022/8668149
  • Weimer D., Scholz-Reiter B. and Shpitalni M., 2016. Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP annals, 65(1), 417-420. https://doi.org/10.1016/j.cirp.2016.04.072
  • Wu G., Kwak H., Jang S., Xu K. and Xu J., 2008. Design of online surface inspection system of hot rolled strips. In 2008 IEEE International Conference on Automation and Logistics, 2291-2295. https://doi.org/10.1109/ICAL.2008.4636548
  • Xiao L., Wu B., Hu Y., 2020. Surface Defect Detection Using Image Pyramid. IEEE Sensors Journal, 20, 7181–7188. https://doi.org/10.1109/JSEN.2020.2977366
  • Xing J., Jia M., 2021. A convolutional neural network-based method for workpiece surface defect detection. Measurement, 176, 109185. http://dx.doi.org/10.1016/j.measurement.2021.109185
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

Hikmetcan Özcan 0000-0002-7146-203X

Hakan Tuğrul Gençtürk 0000-0002-2736-271X

Gülay Genç 0009-0009-7431-9122

Taha Erdem Yıldırım 0009-0002-7219-5006

Fatih Durmuş 0009-0001-3915-8847

Atakan Gürleyen 0009-0007-9344-766X

Erken Görünüm Tarihi 14 Nisan 2024
Yayımlanma Tarihi 29 Nisan 2024
Gönderilme Tarihi 21 Eylül 2023
Yayımlandığı Sayı Yıl 2024 Cilt: 24 Sayı: 2

Kaynak Göster

APA Özcan, H., Gençtürk, H. T., Genç, G., Yıldırım, T. E., vd. (2024). Gerçek Zamanlı Kusur Tespiti: LPG Tüplerinin Yüzeylerinde Kirlilikleri Tanımlama için Görüntü İşleme ve Makine Öğrenimi Teknikleri ile Yenilikçi Bir Yaklaşım. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 24(2), 330-340. https://doi.org/10.35414/akufemubid.1364153
AMA Özcan H, Gençtürk HT, Genç G, Yıldırım TE, Durmuş F, Gürleyen A. Gerçek Zamanlı Kusur Tespiti: LPG Tüplerinin Yüzeylerinde Kirlilikleri Tanımlama için Görüntü İşleme ve Makine Öğrenimi Teknikleri ile Yenilikçi Bir Yaklaşım. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. Nisan 2024;24(2):330-340. doi:10.35414/akufemubid.1364153
Chicago Özcan, Hikmetcan, Hakan Tuğrul Gençtürk, Gülay Genç, Taha Erdem Yıldırım, Fatih Durmuş, ve Atakan Gürleyen. “Gerçek Zamanlı Kusur Tespiti: LPG Tüplerinin Yüzeylerinde Kirlilikleri Tanımlama için Görüntü İşleme Ve Makine Öğrenimi Teknikleri Ile Yenilikçi Bir Yaklaşım”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 24, sy. 2 (Nisan 2024): 330-40. https://doi.org/10.35414/akufemubid.1364153.
EndNote Özcan H, Gençtürk HT, Genç G, Yıldırım TE, Durmuş F, Gürleyen A (01 Nisan 2024) Gerçek Zamanlı Kusur Tespiti: LPG Tüplerinin Yüzeylerinde Kirlilikleri Tanımlama için Görüntü İşleme ve Makine Öğrenimi Teknikleri ile Yenilikçi Bir Yaklaşım. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 24 2 330–340.
IEEE H. Özcan, H. T. Gençtürk, G. Genç, T. E. Yıldırım, F. Durmuş, ve A. Gürleyen, “Gerçek Zamanlı Kusur Tespiti: LPG Tüplerinin Yüzeylerinde Kirlilikleri Tanımlama için Görüntü İşleme ve Makine Öğrenimi Teknikleri ile Yenilikçi Bir Yaklaşım”, Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, c. 24, sy. 2, ss. 330–340, 2024, doi: 10.35414/akufemubid.1364153.
ISNAD Özcan, Hikmetcan vd. “Gerçek Zamanlı Kusur Tespiti: LPG Tüplerinin Yüzeylerinde Kirlilikleri Tanımlama için Görüntü İşleme Ve Makine Öğrenimi Teknikleri Ile Yenilikçi Bir Yaklaşım”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 24/2 (Nisan 2024), 330-340. https://doi.org/10.35414/akufemubid.1364153.
JAMA Özcan H, Gençtürk HT, Genç G, Yıldırım TE, Durmuş F, Gürleyen A. Gerçek Zamanlı Kusur Tespiti: LPG Tüplerinin Yüzeylerinde Kirlilikleri Tanımlama için Görüntü İşleme ve Makine Öğrenimi Teknikleri ile Yenilikçi Bir Yaklaşım. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2024;24:330–340.
MLA Özcan, Hikmetcan vd. “Gerçek Zamanlı Kusur Tespiti: LPG Tüplerinin Yüzeylerinde Kirlilikleri Tanımlama için Görüntü İşleme Ve Makine Öğrenimi Teknikleri Ile Yenilikçi Bir Yaklaşım”. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, c. 24, sy. 2, 2024, ss. 330-4, doi:10.35414/akufemubid.1364153.
Vancouver Özcan H, Gençtürk HT, Genç G, Yıldırım TE, Durmuş F, Gürleyen A. Gerçek Zamanlı Kusur Tespiti: LPG Tüplerinin Yüzeylerinde Kirlilikleri Tanımlama için Görüntü İşleme ve Makine Öğrenimi Teknikleri ile Yenilikçi Bir Yaklaşım. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi. 2024;24(2):330-4.