Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks

Essid, Oumayma; Laga, Hamid; Samir, Chafik

doi:10.1371/journal.pone.0203192

Cited by 43 publications

(15 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…14 With the rise of the interest in deep learning, this specific technology was also applied to a wide range of AVI tasks, for example, structural inspection, 15 surface defect detection and recognition, 16 defect classification system, 17 and others. [18][19][20][21] Even design tools for the creation of deep convolutional networks for AVI were developed. 22 To conclude, deep learning is being applied to a wide range of tasks inside the AVI field, starting with detection, classification, and recognition for the purpose of defect inspection and finishing with full-scale automated production support.…”

Section: Related Workmentioning

confidence: 99%

Classification using a three-dimensional sensor in a structured industrial environment

Mikhailov

Jovančević²,

Mokhtari³

2020

J. Electron. Imag.

View full text Add to dashboard Cite

Usage of a three-dimensional (3-D) sensor and point clouds provides various benefits over the usage of a traditional camera for industrial inspection. We focus on the development of a classification solution for industrial inspection purposes using point clouds as an input. The developed approach employs deep learning to classify point clouds, acquired via a 3-D sensor, the final goal being to verify the presence of certain industrial elements in the scene. We possess the computer-aided design model of the whole mechanical assembly and an in-house developed localization module provides initial pose estimation from which 3-D point clouds of the elements are inferred. The accuracy of this approach is proved to be acceptable for industrial usage. Robustness of the classification module in relation to the accuracy of the localization algorithm is also estimated. Fig. 1 (a) Robot-based inspection system with three cameras mounted on an end effector and (b) Ensenso N35 3-D sensor.

show abstract

Section: Related Workmentioning

confidence: 99%

Classification using a three-dimensional sensor in a structured industrial environment

Mikhailov

Jovančević²,

Mokhtari³

2020

J. Electron. Imag.

View full text Add to dashboard Cite

show abstract

“…Yang et al [14] propose an end-to-end surface quality detection method based on deep convolutional neural networks (CNNs) to improve the accuracy and efficiency of VDR surface quality detection. Essid et al [15] develop a new machine vision framework for efficient detection and classification of manufacturing defects in metal boxes. e results show that the proposed autoencoder deep neural network (DNN) architecture can not only classify manufacturing defects, but also localize them with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Bearing Defect Detection with Unsupervised Neural Networks

Zuo

Danchao³

et al. 2021

Shock and Vibration

View full text Add to dashboard Cite

Bearings always suffer from surface defects, such as scratches, black spots, and pits. Those surface defects have great effects on the quality and service life of bearings. Therefore, the defect detection of the bearing has always been the focus of the bearing quality control. Deep learning has been successfully applied to the objection detection due to its excellent performance. However, it is difficult to realize automatic detection of bearing surface defects based on data-driven-based deep learning due to few samples data of bearing defects on the actual production line. Sample preprocessing algorithm based on normalized sample symmetry of bearing is adopted to greatly increase the number of samples. Two different convolutional neural networks, supervised networks and unsupervised networks, are tested separately for the bearing defect detection. The first experiment adopts the supervised networks, and ResNet neural networks are selected as the supervised networks in this experiment. The experiment result shows that the AUC of the model is 0.8567, which is low for the actual use. Also, the positive and negative samples should be labelled manually. To improve the AUC of the model and the flexibility of the samples labelling, a new unsupervised neural network based on autoencoder networks is proposed. Gradients of the unlabeled data are used as labels, and autoencoder networks are created with U-net to predict the output. In the second experiment, positive samples of the supervised experiment are used as the training set. The experiment of the unsupervised neural networks shows that the AUC of the model is 0.9721. In this experiment, the AUC is higher than the first experiment, but the positive samples must be selected. To overcome this shortage, the dataset of the third experiment is the same as the supervised experiment, where all the positive and negative samples are mixed together, which means that there is no need to label the samples. This experiment shows that the AUC of the model is 0.9623. Although the AUC is slightly lower than that of the second experiment, the AUC is high enough for actual use. The experiment results demonstrate the feasibility and superiority of the proposed unsupervised networks.

show abstract

“…9,18 In addition, automatic inspection and defect detection based on deep learning CNNs have been widely adopted in many fields. 19 CNN methods have been shown to achieve high throughput quality control during the manufacture of metallic rails 20 and steel surfaces, 21 demonstrating their widespread adoption.…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning Convolutional Neural Networks for Internal Tablet Defect Detection: High Accuracy, Throughput, and Adaptability

Kittikunakorn

Sorman³

et al. 2020

Journal of Pharmaceutical Sciences

View full text Add to dashboard Cite

Automatic detection and classification of manufacturing defects in metal boxes using deep neural networks

Cited by 43 publications

References 30 publications

Classification using a three-dimensional sensor in a structured industrial environment

Classification using a three-dimensional sensor in a structured industrial environment

Bearing Defect Detection with Unsupervised Neural Networks

Application of Deep Learning Convolutional Neural Networks for Internal Tablet Defect Detection: High Accuracy, Throughput, and Adaptability

Contact Info

Product

Resources

About