Filtered selective search and evenly distributed convolutional neural networks for casting defects recognition

Ji, Xiaoyuan; Yan, Qiuyu; Huang, Dong; Wu, Bo; Xu, Xiaojing; Zhang, Aibin; Liao, Guanglan; Zhou, Jianxin; Wu, Menghuai

doi:10.1016/j.jmatprotec.2021.117064

Cited by 24 publications

(8 citation statements)

References 9 publications

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“…Casting is a manufacturing process finding applications in complex industries, such as aerospace [2], [35] and automobile [4], [36] industries, and with materials, such as aluminum [37], [38] and titanium [2] alloys. Due to the limitations of the manufacturing techniques [37], castings can host several defects, such as holes and flaws, gas cavities, shrinks, slags, cracks, high-and low-inclusions, wrinkles, casting fins, shrinkage-holes, and incomplete fusion [2], [35], [36], which can lead to catastrophic failures of critical mechanical components [4], [37]. Therefore, it is crucial to implement a non-destructive testing system to detect internal and surface defects early in the manufacturing process to reduce the risks and save time and costs [36], [38].…”

Section: B Castingmentioning

confidence: 99%

“…In casting assessment, most of the studies consider the problem as a binary task (e.g., binary classification or segmentation) to differentiate between defective and non-defective castings. There are also some cases where more than one type of defects define a multi-class detection or segmentation problem [2].…”

Section: B Castingmentioning

confidence: 99%

“…The need of having a non-destructive procedure for examining the interior of objects to assess their structural patterns or constituent contents has resulted in many applications of X-ray technology in different fields. While the medical field was one of the first to use the technology for assessing the inner parts of the body [1], the use of X-ray technology is expanding considerably for industrial and security purposes [2], [3]. Factories can now assess whether there are anomalies or defects inside a product without destroying it [4], and border patrol officers at security gates can check for forbidden objects inside baggage without opening them [5].…”

Section: Introductionmentioning

confidence: 99%

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Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learningbased object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

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Section: B Castingmentioning

confidence: 99%

Section: B Castingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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“…Image Feature Region Extraction is to select potential Regions of Interest (ROI) or directly generate Region Proposal Network (RPN) for ROI pooling. Its function is to render a proposed feature map and input it into the downstream network by BING [7] and Selective Search [8]. Object Recognition with Locating Regression can detect all the external rectangular boxes and their corresponding named labels.…”

Section: A Vrd Basic Processmentioning

confidence: 99%

Visual Relationship Detection With Image Position and Feature Information Embedding and Fusion

Peng¹,

Zhang²,

Huang³

2022

IEEE Access

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Visual relationship detection (VRD) is an important direction in the field of image processing, and it is a research task to explore object relationships based on object recognition and localization regression. At the same time, it is also one of the key contents of scene graph generation and construction of multimodal knowledge graph. In order to better improve the visual relationship detection effect, the fusion method of image location and feature information embedding is adopted on the vrd dataset. First, analyze the causes of errors in existing methods, and build a pre-training pre-dataset vrd_P for data enhancement; then, combine the characteristics of the vrd dataset to establish an entities relationship network (Entities-net) to guide target recognition; finally, a visual relationship detection model of LTransE is proposed to achieve the joint representation of target features and target locations for relationship detection. The results show that the method of embedding fusion of image location and feature information can effectively improve the visual relationship detection effect. INDEX TERMSVRD; Data-enhancement; VTransE; scene graph generation; LTransE Dog Person front FIGURE 1. An example of the visual relationship in a picture.

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“…Although previous researchers have made significant efforts in detecting defects in industrial products using the CNN model [15][16][17][18][19][20], little attention has been paid to recognise similar industrial machining parts. The issue that the human operator faces on the manufacturing floor is not only related to defects, but also to misclassification of machining components.…”

Section: Introductionmentioning

confidence: 99%

Application of Convolutional Neural Networks for Binary Recognition Task of Two Similar Industrial Machining Parts

Hafizh¹,

Rasib²,

Abdullah³

et al. 2021

IJACSA

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Misclassifying parts in the small-medium manufacturing enterprise can lead to serious consequences. Manual inspection, as currently practiced, allows for compromises in product traceability. Due to this condition, inspection of the part's number is not digitally visible. Due to a lack of modern traceability, customers receive incorrect parts, and the same incidents continue to occur. It is essential to transform manual inspections into digital and automated ones. AI-based technologies have recently been employed to enable a smart and intelligent recognition system for industrial machining parts. Convolutional Neural Networks (CNN) are widely used for image recognition tasks and are gaining popularity as deep learning algorithms. In this paper, a CNN model is used to perform binary recognition on two similar industrial machining parts. The model has been trained to recognise two classes of machining parts: Parts A and B. The dataset used to train the model includes both original and augmented images, with a total of 2447 images for both classes. The performance metrics have been measured during the training process, and 10 experiments have been conducted to evaluate the performance of the model. The test results reveal that the CNN model achieves 98% mean accuracy, 97.1% precision for Part A, 99% precision for part B and 0.982 AUC value. The results demonstrate the effectiveness of the CNN-based recognition of parts. It offers an effective alternative and is a compelling method for quality assurance in small-medium manufacturing enterprises.

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Filtered selective search and evenly distributed convolutional neural networks for casting defects recognition

Cited by 24 publications

References 9 publications

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Visual Relationship Detection With Image Position and Feature Information Embedding and Fusion

Application of Convolutional Neural Networks for Binary Recognition Task of Two Similar Industrial Machining Parts

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