Approaches for improvement of the X-ray image defect detection of automobile casting aluminum parts based on deep learning

Du, Wangzhe; Shen, Hongyao; Fu, Jianzhong; Zhang, Ge; He, Quan

doi:10.1016/j.ndteint.2019.102144

Cited by 126 publications

(55 citation statements)

References 18 publications

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“…While a classifier gives only the defect class as output, an object detector can give the defect class as well as the location of the defect (bounding box coordinates) in the input image as output. Wangzhe Du et al[46] proposed an object detector-based casting defect detection for aluminium automobile parts. Their proposed model can identify and locate the defects but cannot classify the type of the defect.…”

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confidence: 99%

Transfer Learning With CNN for Classification of Weld Defect

et al. 2021

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Traditional Image Processing Techniques (IPT), used for automating the detection and classification of weld defects from radiography images, have their own limitations, which can be overcome by Deep Neural Networks (DNN). DNN produces considerably good results in fields which offer big dataset for it to train. DNN trained with small datasets by conventional methods produces less accurate results. This limits the use of DNN in many fields. This study focuses to overcome this limitation, by adopting transfer learning using Pre-trained deep convolutional neural networks. By this method, a weld defect radiographic image classifier, which can classify 14 types of weld defects, was constructed. 940 Image patches of weld defects were manually collected and labelled from GDXray database. Subsequently the features of this weld defect dataset were extracted using VGG16 and ResNet50 CNNs, both pre-trained on the ImageNet database. Then machine learning models such as Logistic Regression, Support Vector Machine (SVM) and Random Forest were trained on these extracted features. The Classifier based on SVM trained on features extracted by ResNet50 outperforms the other counter parts with an accuracy of 98%. In all these cases, transfer learning improves performance and reduces the training time and computational system requirements.INDEX TERMS Classifier, convolutional neural networks (CNN), radiographic images, transfer learning, weld defect detection.

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confidence: 99%

Transfer Learning With CNN for Classification of Weld Defect

et al. 2021

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“…He Di et al [22] trained a classifier for strip defects recognition based on convolutional auto-encoder (CAE) and a devised semi-supervised Generative Adversarial Networks. To overcome the trivial image pre-processing and feature extraction process, Wangzhe Du et al [23] presented an X-ray defect detection system based on the Feature Pyramid Network and a data augmentation method for model generalization training. Veitch-Michaelis et al [24] studied the 3D cracks recognition method through the combination of morphological detection and SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

A Deep-Learning-based 3D Defect Quantitative Inspection System in CC Products Surface

Zhao

Zhang

et al. 2020

Sensors

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To create an intelligent surface region of interests (ROI) 3D quantitative inspection strategy a reality in the continuous casting (CC) production line, an improved 3D laser image scanning system (3D-LDS) was established based on binocular imaging and deep-learning techniques. In 3D-LDS, firstly, to meet the requirements of the industrial application, the CCD laser image scanning method was optimized in high-temperature experiments and secondly, we proposed a novel region proposal method based on 3D ROI initial depth location for effectively suppressing redundant candidate bounding boxes generated by pseudo-defects in a real-time inspection process. Thirdly, a novel two-step defects inspection strategy was presented by devising a fusion deep CNN model which combined fully connected networks (for defects classification/recognition) and fully convolutional networks (for defects delineation). The 3D-LDS’ dichotomous inspection method of defects classification and delineation processes are helpful in understanding and addressing challenges for defects inspection in CC product surfaces. The applicability of the presented methods is mainly tied to the surface quality inspection for slab, strip and billet products.

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“…In recent years, the favorable conditions have been created for the accurate and rapid detection with the development and application of deep learning. Du et al [ 19 ] modified the Faster-RCNN algorithm by using feature pyramid network (FPN), used different data enhancement methods to make up for the lack of the images in the dataset and improved the defect detection accuracy of X-ray image on automotive castings. Zhao et al [ 20 ] segmented the collected car wheel images and enhanced the image contrast and defect characteristics through image processing technology.…”

Section: Introductionmentioning

confidence: 99%

Pointer Defect Detection Based on Transfer Learning and Improved Cascade-RCNN

Zhao

Huang

et al. 2020

Sensors

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To meet the practical needs of detecting various defects on the pointer surface and solve the difficulty of detecting some defects on the pointer surface, this paper proposes a transfer learning and improved Cascade-RCNN deep neural network (TICNET) algorithm for detecting pointer defects. Firstly, the convolutional layers of ResNet-50 are reconstructed by deformable convolution, which enhances the learning of pointer surface defects by feature extraction network. Furthermore, the problems of missing detection caused by internal differences and weak features are effectively solved. Secondly, the idea of online hard example mining (OHEM) is used to improve the Cascade-RCNN detection network, which achieve accurate classification of defects. Finally, based on the fact that common pointer defect dataset and pointer defect dataset established in this paper have the same low-level visual characteristics. The network is pre-trained on the common defect dataset, and weights are transferred to the defect dataset established in this paper, which reduces the training difficulty caused by too few data. The experimental results show that the proposed method achieves a 0.933 detection rate and a 0.873 mean average precision when the threshold of intersection over union is 0.5, and it realizes high precision detection of pointer surface defects.

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Approaches for improvement of the X-ray image defect detection of automobile casting aluminum parts based on deep learning

Cited by 126 publications

References 18 publications

Transfer Learning With CNN for Classification of Weld Defect

Transfer Learning With CNN for Classification of Weld Defect

A Deep-Learning-based 3D Defect Quantitative Inspection System in CC Products Surface

Pointer Defect Detection Based on Transfer Learning and Improved Cascade-RCNN

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