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

Year 2024, Volume: 24 Issue: 2, 330 - 340, 29.04.2024

Hikmetcan Özcan , Hakan Tuğrul Gençtürk , Gülay Genç , Taha Erdem Yıldırım , Fatih Durmuş , Atakan Gürleyen

https://doi.org/10.35414/akufemubid.1364153

Abstract

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.

Keywords

Gerçek zamanlı kusur tespiti, Görüntü İşleme, LGP Tüpü, Makine öğrenmesi

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

Abstract

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.

Keywords

Real time defect detection, Image processing, Gas cylinder, Machine learning

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