Real time fabric defect detection system on Matlab and C++/Opencv platforms

Hanbay, Kazım; Golgiyaz, Sedat; Talu, Muhammed Fatih

doi:10.1109/idap.2017.8090180

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…Comparisons with the other works found in the scientific literature (e.g. [6], [7], [8], [9], [10], and [11]) cannot be directly made, inasmuch as this module focuses in the detection of defects, rather than…”

Section: 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃+𝑇𝑁 𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁mentioning

confidence: 99%

“…They reached an accuracy of 96%. Hanbay et al [8] compared the performances of Matlab and C++ with a method for defect detection that consisted in combining histogram oriented gradients (HOG) for feature extraction and artificial neural networks for classification, which reached an accuracy of 93%. It was also possible to conclude that C++ is more than 18 times faster than Matlab in processing the images, while using the same algorithmic approach.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using deep learning to detect the presence/absence of defects on leather: on the way to build an industry-driven approach

Adão

Gonzalez

Castilla

et al. 2022

J. Phys.: Conf. Ser.

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In textile/leather manufacturing environments, as in many other industrial contexts, quality inspection is an essential activity that is commonly performed by human operators. Error, fatigue, ergonomic issues, and related costs associated to this fashion of carrying out fabric validation are aspects concerning companies’ strategists, whose mission includes to watch over the physical integrity of their employees, while aiming at enhanced quality control methods implementation towards profit maximization. Considering these challenges from a technical/scientific perspective, machine/deep learning approaches have been showing great skills in adapting a wide range of contexts and, in particular, industrial environments, complementing traditional computer vision methods with characteristics such as increased accuracy while dealing with image classification and segmentation problems, capacity for continuous learning from experts input and feedback, flexibility to easily scale training for new contextualization classes – unknown types of occurrences relevant to characterize a given problem –, among other advantages. The goal of crossing deep learning strategies with fabric inspection processes is pursued in this paper. After providing a brief but representative characterization of the targeted industrial context, in which, typically, fabric rolls of raw-material mats must be processed at a relatively low latency, an Automatic Optical Inspection (AOI) system architecture designed for such environments is revisited [1], for contextualization purposes. Afterwards, a set of deep learning-oriented training methods/processes is proposed in combination with neural networks built based on Xception architecture, towards the implementation of one of the components that integrate the aforementioned system, from which is expected the identification of presence/absence of defective textile/leather raw material at a low-latency. Several models powered by Xception were trained with different tunning parameters, resorting to datasets variations that were set up from raw images of leather, following different annotation strategies (meticulous and rough). The model that performed better reached 96% of accuracy.

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Section: 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃+𝑇𝑁 𝑇𝑃+𝑇𝑁+𝐹𝑃+𝐹𝑁mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using deep learning to detect the presence/absence of defects on leather: on the way to build an industry-driven approach

Adão

Gonzalez

Castilla

et al. 2022

J. Phys.: Conf. Ser.

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show abstract

“…Alev görüntüsü ile icra edilen uygulamalar genel olarak 3 tip olmaktadır. Yanma sürecinin izlenmesi-kontrol edilmesi (González-Cencerrado et al 2013;Hanbay et al 2017;Wang et al 2017), emisyon tahmini (Baek et al 2001;Li et al 2014;Wang et al 2002) ve alev görüntüsünden sıcaklığının tahmin edilmesidir (Bonefacic et al 2015;Lou et al 2007;Xiangyu et al 2018). Alev görüntüsü çalışmalarında genel olarak CCD(charge-coupled device) kamera kullanılmasına rağmen spektroskopik görüntüleme sistemi kullanan çalışmalarda bulunmaktadır (Krabicka et al 2010;Li et al 2016).…”

Section: Introductionunclassified

Görüntü İşleme ve Makine Öğrenmesi Yöntemleri ile Baca Gazı Sıcaklığının Tahmin Edilmesi

Golgiyaz

Talu

Onat³

2019

European Journal of Science and Technology

Self Cite

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ÖzBu makalede, küçük ölçekli fındık kömürü yakıtlı brülörde baca gazı sıcaklığı tahmini ile ilgili deneysel bir çalışma sunulmaktadır. Baca gazı sıcaklığı yakıt türüne göre belli bir aralıkta olması gerekir aksi durumda kazanda korozyona sebep olmaktadır. Bu çalışma kapsamında alev görüntüsünden öznitelikler elde edilmiştir. Bu öznitelikler ve DVR modeli ile baca gazı sıcaklığı tahmin edilmiştir. Alev görüntüsü CCD kamera ile alınmıştır. Aynı zamanda referans baca gazı sıcaklığı, baca gazı analizörü ile alınmıştır. Alev görüntüsü ve sıcaklık değeri aynı bilgisayara kaydedilmiştir. Alev görüntüsü gri seviye görüntüsüne çevrilerek öznitelikler elde edilmiştir. Öznitelikler elde edilirken alev görüntüsünün yoğunluk dağılımı kullanılmıştır. Bu işlem için iki tip dağılım kullanılmıştır. Birincisi görüntünün histogramı alınarak konumdan bağımsız yoğunluk dağılımının elde edilmesidir. İkincisi satır ve sütun toplamlarını kullanarak uzamsal yoğunluk dağılımının elde edilmesidir. Bu iki özniteliğin kombinasyonlarından elde edilen öznitelikler 6 çeşit DVR modeli ile gerçekleştirilmiştir. En iyi sonuçlar, her iki dağılımdan elde edilen özniteliklerin birlikte kullanıldığı öznitelik çıkarma yöntemi için kübik DVR modeli ile elde edilmiştir. Önerilen modelde baca sıcaklığı (T °C) R = 0.97 doğruluk ile tahmin edilmiştir. Elde edilen sonuçlar baca gazı sıcaklığı ile alev görüntüsü arasında yüksek oranda bir ilişki olduğunu göstermektedir.

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“…8 , 9 The frequency domain method analyzes the image features through the spectrogram, but the detection effect for complex texture is poor. 10 , 11 The model method takes the fabric texture as a random process model, which can describe texture well. However, it has a large calculation volume and the detection accuracy for minor size defects is low.…”

mentioning

confidence: 99%

Fabric defect detection using cartoon–texture image decomposition model and visual saliency method

Zhu

Xin

Deng

et al. 2023

Textile Research Journal

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Fabric defect detection is of great importance in the modern textile industry. In actual production, subjective factors usually affect manual detection, which is prone to problems such as false and missed detection. With the development of computer vision, a large number of fabric defect automatic detection algorithms have been proposed. Traditional algorithms rely too heavily on setting parameters manually, while deep learning algorithms have expensive training and computing costs. Given these limitations, a new fabric defect detection method based on the latest cartoon texture image decomposition model, visual saliency algorithm, and mathematical morphology, is proposed in this article. A digital image acquisition system is also designed and constructed. Therefore, the self-made dataset used in the experiment is composed of self-collected images and network public images. To further evaluate the performance of the proposed method, this study conducted fabric defect detection experiments and comparison experiments based on the self-made dataset. The results show that this method can successfully detect fabric defects, having high detection accuracy and efficiency. Combining subjective vision and objective evaluation, comparison experiments prove that this method is superior to other common fabric defect detection methods and has the highest value of accuracy and F1-score. This research provides a new method and technical support for fabric defect detection and other fields.

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Real time fabric defect detection system on Matlab and C++/Opencv platforms

Cited by 9 publications

References 18 publications

Using deep learning to detect the presence/absence of defects on leather: on the way to build an industry-driven approach

Using deep learning to detect the presence/absence of defects on leather: on the way to build an industry-driven approach

Görüntü İşleme ve Makine Öğrenmesi Yöntemleri ile Baca Gazı Sıcaklığının Tahmin Edilmesi

Fabric defect detection using cartoon–texture image decomposition model and visual saliency method

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