Detection of crack eggs by image processing and soft-margin support vector machine

Wu, Liaoni; Wang, Qiaohua; Jie, Dengfei; Wang, Shucai; Zhu, Zhongkui; Xiong, Lihua

doi:10.3233/jcm-170767

Cited by 17 publications

(15 citation statements)

References 19 publications

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“…After the calculation of the difference/indifference subspaces and the common vector, the training phase of CVA is completed. In the test phase of CVA, the vector, which will be classified in the pattern test set, is tested using the following decision criteria and assigned to the appropriate class: (5) Where is the number of classes, are the eigenvectors spanning the indifference subspace of the class , represents the average vector of the class C, and represents the class to which the unknown vector was assigned as a result of the testing process.…”

Section: Common Vector Approach (Cva)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Egg cracks were attempted to be detected using images taken with the aid of a camera [5]. Several image processing and pattern recognition methods have been applied to eggs under a condition of illuminating light source [5][6][7][8][9][10][11][12][13][14]. As a result of these studies, it was observed that the cracks on eggshells were detected with a success rate of more than 90% using several computer vision methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Detection of Eggshell Cracks Using Different Classifiers

Yumurtacı

Balci

Ergin

et al. 2022

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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Chicken eggs, which are widely consumed in daily life due to their rich nutritional values, are also used in many products. The increasing need for eggs must be met quickly for various circumstances. Eggs are subjected to various impacts and shaken from production to packaging. In some cases, these effects cause an eggshell to crack. While these cracks are sometimes visible, sometimes they are micro-sized and cannot be seen. The crack causes an egg to enter into harmful microorganisms and spoil in a short time. In this study, the acoustic signals generated in consequence of the application using a mechanical contact to eggs were recorded for the duration of 0.2 seconds at 50 kHz sampling frequency with the help of a microphone. To determine the active part in the collected acoustic signal data, a clipping process was implemented by a thresholding process. Thus, the exactly correct moment of mechanical contact on the eggshell was easily detected. Statistical parameters, which are min, max, difference, mean, standard deviation, skewness, and kurtosis values, are extracted from the thresholded data signals so that 7-dimensional feature vectors has been constituted. Finally, the Common Vector Approach (CVA) is applied on the extracted feature vectors, 100% success rate has been achieved for the test data set. The ANN and SVM classifiers in where the same feature vectors are treated were used for the comparison purpose, and exactly the same classification rates are attained; however, the less number of eggs are tested with the ANN and SVM classifiers in the same amount of time. With the proposed mechanical system and classification methodology, it takes about 0.2008 seconds to determine whether the shells of eggs are cracked/intact. Therefore, the proposed combination of the feature vectors based on statistical features and CVA as a classifier for the detection of cracks on eggshells is notably appropriate especially for industrial applications in terms of speed and accuracy aspects.

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Section: Common Vector Approach (Cva)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Detection of Eggshell Cracks Using Different Classifiers

Yumurtacı

Balci

Ergin

et al. 2022

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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“…This shows the difficulties faced when high precision is a factor to consider. Classification of cracked eggs with computer vision systems has proven to be difficult: Wu et al [ 5 ] managed to achieve only 93% validated correct classification using soft-margin support vector machine (SVM) classification.…”

Section: Introductionmentioning

confidence: 99%

Development of a Novel Acoustic Spectroscopy Method for Detection of Eggshell Cracks

et al. 2021

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Non-destructive testing (NDT) for eggshell faults is highly important for the egg industry, as cracked eggs account for around 3% of total production. The most commonly used method at present, candling, is labor intensive, while computer vision systems are expensive and complicated. In this paper, we present a simple, yet efficient, novel method for eggshell crack detection by acoustic spectroscopy. Altogether, 693 sound recordings were evaluated by different classification methods. The results show a cross-validated 2.1% total classification error, with only 0.87% false positive rate, which is the crucial metric for fresh eggs. Adapting the developed method to an industrial setting may lead to a reliable, fast and cost-effective detection method.

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“…Daha sonra görüntü kamera ile alınarak ardından çeşitli görüntü işleme yöntemleri ve örüntü tanıma metotları ile çatlaklar tespit edilmeye çalışılmıştır. Önceki çalışmalar göstermiştir ki, gözle görülebilen çatlaklarda bilgisayarlı görü ile yumurta kabuğundaki çatlaklar %90 üzeri bir başarımla tespit edilebilmiştir [5][6][7][8][9][10]. Ancak bilgisayarlı görü yönteminde yumurta yüzeylerinin düzensizliği ve ışık kaynağının ayarı yumurta kabuğundaki çatlakların tespitinde sorun teşkil ettiği, ayrıca bu yöntemlerin yumurta kabuğundaki mikro çatlakları tespit etmede yetersiz kaldığı rapor edilmiştir [11].…”

Section: Introductionunclassified

Yumurta Kabuğundan Alınan Akustik Sinyalin Dalgacık Paket Dönüşümü ve Entropiye Dayalı Olarak İşlenmesi ve Yapay Sinir Ağlarıyla Çatlağın Belirlenmesi

Balci

Yumurtacı

Yabanova

et al. 2021

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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Endüstride birçok üründe ve evlerimizde, vitaminler ve mineraller bakımından zengin olmasından dolayı yumurta yaygın olarak tüketilmektedir. Artan ihtiyacın hızlı bir şekilde karşılanması için tavuk çiftliklerinde yumurtaların toplanması, ağırlıklarına göre sınıflandırılması, sağlam/çatlak olanların ayrılması, paketlenmesi vb. işlemler için otomasyona geçiş yapılmıştır. Kabuğun çatlak olması durumunda içerisine zararlı mikroorganizmalar kolaylıkla girebileceği gibi yumurta içinin havayla temasından dolayı kısa sürede bozulmasına yol açacaktır. Çatlaklar gözle görülebilecek kadar büyük olabildiği gibi bazen de mikro boyutta olmakta insan gözüyle tespit edilememektedir. Bu çalışmada yumurta kabuğunun çatlak/sağlam olması durumunun sinyal işleme ve makine öğrenme tabanlı tespiti gerçekleştirilmiştir. Mekanik sistem vasıtasıyla kabuğa yapılan darbe neticesindeki oluşan akustik sinyal sistemdeki mikrofonla 50kHz örnekleme frekansında 0.2 sn süresince kayıt altına alınmaktadır. Kabuğu sağlam ve çatlak olan ayrı ayrı 50 yumurta verisi düzenekle kayıt altına alınıp veri seti oluşturulmuştur. Yumurta kabuğuna darbenin uygulanma anından sönümlenene kadarki zamanın tespiti için 0.74V eşik değeri kullanılıp bu değerden sonraki 680 veri alınmıştır. Bu verilere db4 ana dalgacığı ile 2. seviyeden Dalgacık Paket Dönüşümü (DPD) uygulanarak farklı frekanslı detay ve yaklaşım bileşenleri çıkartılmıştır. Her bir bileşenin entropi değeri hesaplanarak 1x4 boyutunda özellik vektörü elde edilmiştir. Çıkartımı yapılan özellik vektörünün yumurta kabuğundaki çatlağın tespitindeki etkinliğini belirlemek için Yapay Sinir Ağı (YSA) kullanılmıştır. %100 başarım elde edilmiş olup bir yumurtanın kabuk çatlak/sağlam belirleme süresi yaklaşık olarak 0.216sn'dir.

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Detection of crack eggs by image processing and soft-margin support vector machine

Cited by 17 publications

References 19 publications

The Detection of Eggshell Cracks Using Different Classifiers

The Detection of Eggshell Cracks Using Different Classifiers

Development of a Novel Acoustic Spectroscopy Method for Detection of Eggshell Cracks

Yumurta Kabuğundan Alınan Akustik Sinyalin Dalgacık Paket Dönüşümü ve Entropiye Dayalı Olarak İşlenmesi ve Yapay Sinir Ağlarıyla Çatlağın Belirlenmesi

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