Machine-learning-based surface defect detection and categorisation in high-precision foundry

Pastor-López, Iker; Santos, Igor; Santamaría-Ibirika, Aitor; Salazar, Mikel; de-la-Peña-Sordo, Jorge; Bringas, Pablo García

doi:10.1109/iciea.2012.6360934

Cited by 16 publications

(13 citation statements)

References 19 publications

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“…CONCLUSIONS AND FUTURE WORK In this paper, we have presented a novel collectiveclassification-based approach for surface defect detection in iron castings. This method requires less labelling effort than presented previous work [7] based on supervised learning approach.…”

Section: Empirical Validationmentioning

confidence: 80%

“…In order to retrieve and process the casting model data, we develop a simple computer-vision system that is composed of a laser camera with 3D technology, a computer with high data processing capabilities and a robotic arm, already employed in our previous work [7].…”

Section: Data Acquisition and Segmentation Through Machine Visionmentioning

confidence: 99%

“…In our previous work, we proposed a new approach for surface defect detection and categorisation using machinelearning classifiers [7]. Nevertheless, these supervised classifiers require a high number of labelled instances for each class.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Collective classification for the detection of surface defects in automotive castings

Pastor-López

Santos

de-la-Peña-Sordo

et al. 2013

2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA)

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Iron casting production is a very important industry that supplies critical products to other key sectors of the economy. For this reason, these castings are subject to very strict safety controls to ensure their final quality. One of the most common flaws is the appearance of defects on the surface. In particular, our work focuses on three of the most typical defects in iron foundries: inclusions, cold laps and misruns. We propose a new approach that detects these imperfections on the surface by means of a segmentation method that flags the potential defective regions on the casting and, then, applies collective classification techniques to determine whether the regions are defective or not. We show that these classifiers obtain high precision rates whilst decreasing the effort of labelling.

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Section: Empirical Validationmentioning

confidence: 80%

Section: Data Acquisition and Segmentation Through Machine Visionmentioning

confidence: 99%

See 1 more Smart Citation

Collective classification for the detection of surface defects in automotive castings

Pastor-López

Santos

de-la-Peña-Sordo

et al. 2013

2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA)

Self Cite

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“…Por ejemplo, en la industria de la fundición, los autores en [59] utilizan SMOTE para balancear las clases en un conjunto de entrenamiento para la clasificación de defectos en piezas fundidas de metal. Por otro Figura 6.…”

Section: A Sivas Con Técnicas De Muestreounclassified

A Survey on Class Imbalance Learning on Automatic Visual Inspection

Mera

Branch

2014

IEEE Latin Am. Trans.

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The supervised machine learning has been showing very useful for the automatic visual inspection task. However, little has been considered about use traditional machine learning techniques on a domain where the classes are imbalanced. This problem corresponds to dealing with the situation where one class outnumbers the other. Traditional machine learning algorithms trained with imbalance datasets can be biased towards the majority class, thus producing poor predictive accuracy over the minority class. In this paper, we present different approaches to address the class imbalance problem and how these approaches have been used in the context of automatic visual inspection. The literature shows there are few works that consider the class imbalance problem on automatic visual inspection task and it shows that the one class classification technique is the most used.

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“…Image processing systems also use machine learning algorithms. Machinelearning is an active research area within artificial intelligence that focuses on the design and development of new algorithms that allow computers to reason and decide based on data [16]. The most commonly used machine learning classifiers -Bayesian Networks, Decision Trees, K-Nearest Neighbor, Support Vector Machines.…”

Section: Introductionmentioning

confidence: 99%

Research of the Defects in Anesthetic Masks

Laucka¹,

Andriukaitis²

2015

RADIOENGINEERING

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The article concerns the computer-assisted vision system created for the detection of the disposable anesthetic respiratory masks. This article provides the classification of defects which may be common to both rubber and plastic parts of the masks. The defects were divided into groups and the nature of them was investigated. The algorithms and methods for the detection of defective products were based on the segmentation of image and the detection of uneven contours. The experiment results are presented in this work. With reference to the results, the most effective masks' filters were identified. The achieved specificity of the algorithm is 100 % and the sensitivity is 100 %.

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Machine-learning-based surface defect detection and categorisation in high-precision foundry

Cited by 16 publications

References 19 publications

Collective classification for the detection of surface defects in automotive castings

Collective classification for the detection of surface defects in automotive castings

A Survey on Class Imbalance Learning on Automatic Visual Inspection

Research of the Defects in Anesthetic Masks

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