A deep-learning-based approach for automated wagon component inspection

Rocha, Rafael L.; Siravenha, Ana Carolina; Gomes, Ana; Serejo, Gerson L.; Silva, Alexandre F. B.; Rodrigues, Luciano Miller Reis; Braga, J.; Dias, Giovanni; Carvalho, Schubert R.; Souza, Cleidson R. B. de

doi:10.1145/3167132.3167157

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Extensive research is being done in regard to the inspection of train and railroad tracks [13]- [15]. In previous works, Rocha et al [16]- [18], in a partnership with Vale S.A., developed and refined an approach, based on Convolutional Neural Networks, specific for inspection of the pad, one of the components in a wagon wheelset.…”

Section: Introductionmentioning

confidence: 99%

Improving the performance of a SVM+HOG classifier for detection and tracking of wagon components by using geometric constraints

Gonçalves¹,

Zampolo

Barros

et al. 2019

Anais Estendidos Do XXXII Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2019)

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The inspection of train and railway components that can cause derailment plays a key role in rail maintenance. To improve productivity and safety, service providers look for automatic and reliable inspection solutions. Although automatic inspection based on computer vision is a standard concept, such an application challenges development community due to the environmental and logistic factors to be considered. Previous publications presented automatic classifiers to evaluate integrity and placement of wagon components. Although the high classification accuracy reported, ineffective object detection affected the general performance. Our object detector/tracker consists of a descriptor based on the histogram of oriented gradients, a support vector machine classifier, and a set of geometric constraints, which takes in account the ideal trajectory path of the wagon’s components of interest and the distances between them. We detail training and validation procedures, together with the metrics used to assess the performance of the system. Presented results compare two other techniques with our approach, which exhibits a fair trade-off between reliability and computational complexity for the application of wagon component detection.

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Section: Introductionmentioning

confidence: 99%

Improving the performance of a SVM+HOG classifier for detection and tracking of wagon components by using geometric constraints

Gonçalves¹,

Zampolo

Barros

et al. 2019

Anais Estendidos Do XXXII Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2019)

Self Cite

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“…The CNN architecture that composes the ILEC proposal was previously published in (ROCHA et al, 2017) and (ROCHA et al, 2018), and consist of two convolutional layers (conv1 and conv2), two fully connected layers (fully1 and fully2) and one MaxPooling layer (pool1). The sequence that the layers has been implemented is conv1, conv2, pool1, fully1 and fully2.…”

Section: Cnn Architecturementioning

confidence: 99%

“…In this context, this work seeks the use of CNN together with ensembles to correctly classify, through images, the condition in which a wagon component is found. More specifically, we used three types of classifiers to evaluate the pads: (i) the first one is CNN (LeNet, CNN architecture described in (ROCHA et al, 2018); (ii) Ensemble of Multi Layer Perceptron (MLP); (iii) Ensemble of Convolutional Neural Networks. The latter being our main proposal for solving the problem described.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary ensembles for imbalanced learning

Fernandes¹

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In many real classification problems, the data set used for model induction is significantly imbalanced. This occurs when the number of examples of some classes is much lower than the other classes. Imbalanced datasets can compromise the performance of most classical classification algorithms. The classification models induced by such datasets usually present a strong bias towards the majority classes, tending to classify new instances as belonging to these classes. A commonly adopted strategy for dealing with this problem is to train the classifier on a balanced sample from the original dataset. However, this procedure can discard examples that could be important for a better class discrimination, reducing classifier efficiency. On the other hand, in recent years several studies have shown that in different scenarios the strategy of combining several classifiers into structures known as ensembles has proved to be quite effective. This strategy has led to a stable predictive accuracy and, in particular, to a greater generalization ability than the classifiers that make up the ensemble. This generalization power of classifier ensembles has been the focus of research in the imbalanced learning field in order to reduce the bias toward the majority classes, despite the complexity involved in generating efficient ensembles. Optimization meta-heuristics, such as evolutionary algorithms, have many applications for ensemble learning, although they are little used for this purpose. For example, evolutionary algorithms maintain a set of possible solutions and diversify these solutions, which helps to escape out of the local optimal. In this context, this thesis investigates and develops approaches to deal with imbalanced datasets, using ensemble of classifiers induced by samples taken from the original dataset. More specifically, this theses propose three solutions based on evolutionary ensemble learning and a fourth proposal that uses a pruning mechanism based on dominance ranking, a common concept in multiobjective evolutionary algorithms. Experiments showed the potential of the developed solutions.

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A Literature Review for the Application of Artificial Intelligence in the Maintenance of Railway Operations with an Emphasis on Data

Pappaterra

2022

Communications in Computer and Information Science

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A deep-learning-based approach for automated wagon component inspection

Cited by 4 publications

References 27 publications

Improving the performance of a SVM+HOG classifier for detection and tracking of wagon components by using geometric constraints

Improving the performance of a SVM+HOG classifier for detection and tracking of wagon components by using geometric constraints

Evolutionary ensembles for imbalanced learning

A Literature Review for the Application of Artificial Intelligence in the Maintenance of Railway Operations with an Emphasis on Data

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