scholarly journals High-Precision Detection of Defects of Tire Texture Through X-ray Imaging Based on Local Inverse Difference Moment Features

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2524 ◽  
Author(s):  
Guo Zhao ◽  
Shiyin Qin
Keyword(s):  
High Precision ◽  
Defect Detection ◽  
Detection Algorithm ◽  
Image Sensor ◽  
Production Quality ◽  
Background Suppression ◽  
Texture Image ◽  
X Ray ◽  
Feature Map ◽  
Detection Of Defects

Automatic defect detection is an important and challenging issue in the tire industrial quality control. As is well known, the production quality of tire is directly related to the vehicle running safety and passenger security. However, it is difficult to inspect the inner structure of tire on the surface. This paper proposes a high-precision detection of defects of tire texture image obtained by X-ray image sensor for tire non-destructive inspection. In this paper, the feature distribution generated by local inverse difference moment (LIDM) features is proposed to be an effective representation of tire X-ray texture image. Further, the defect feature map (DFM) may be constructed by computing the Hausdorff distance between the LIDM feature distributions of original tire image and each sliding image patch. Moreover, DFM may be enhanced to improve the robustness of defect detection algorithm by a background suppression. Finally, an effective defect detection algorithm is proposed to achieve the pixel-level detection of defects with high precision over the enhanced DFM. In addition, the defect detection algorithm is not only robust to the noise in the background, but also has a more powerful capability of handling different shapes of defects. To validate the performance of our proposed method, two kinds of experiments about the defect feature map and defect detection are conducted to demonstrate its good performance. Moreover, a series of comparative analyses demonstrate that the proposed algorithm can accurately detect the defects and outperforms other algorithms in terms of various quantitative metrics.

A CMOS Image Sensor with a Novel Passive Pixel Array and High Precision Current Amplifier for a Compact Digital X-ray Detector

2020 ◽  
Vol 10 (11) ◽  
pp. 2745-2753
Author(s):  
Jimin Cheon ◽  
Dongmyung Lee ◽  
Hojong Choi
Keyword(s):  
High Precision ◽  
Dynamic Range ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Total Power ◽  
Cmos Process ◽  
Current Amplifier ◽  
X Ray ◽  
Total Power Consumption ◽  
Pixel Array

An active pixel sensor (APS) in a digital X-ray detector is the dominant circuitry for a CMOS image sensor (CIS) despite its lower fill factor (FF) compared to that of a passive pixel sensor (PPS). Although the PPS provides higher FF, its overall signal-to-noise ratio (SNR) is lower than that of the APS. The required high resolution and small focal plane can be achieved by reducing the number of transistors and contacts per pixel. We proposed a novel passive pixel array and a high precision current amplifier to improve the dynamic range (DR) without minimizing the sensitivity for diagnostic compact digital X-ray detector applications. The PPS can be an alternative to improve the FF. However, size reduction of the feedback capacitor causes degradation of SNR performance. This paper proposes a novel PPS based on readout and amplification circuits with a high precision current amplifier to minimize performance degradation. The expected result was attained with a 0.35-μm CMOS process parameter with power supply voltage of 3.3 V. The proposed PPS has a saturation signal of 1.5 V, dynamic range of 63.5 dB, and total power consumption of 13.47 mW. Therefore, the proposed PPS readout circuit improves the dynamic range without sacrificing the sensitivity.

scholarly journals Development of High-Accuracy Defect Detection Algorithm for X-Ray Welding Image Inspection under Strong Noise, Low Contrast and Few Samples

2021 ◽  
Vol 87 (12) ◽  
pp. 1003-1007
Author(s):  
Kenji IWATA ◽  
Tomohiro MATSUMOTO ◽  
Keiko AOYAMA ◽  
Keisuke KAJIKAWA ◽  
Koji GOTO ◽  
...  
Keyword(s):  
Defect Detection ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Low Contrast ◽  
X Ray ◽  
Image Inspection ◽  
Welding Image

scholarly journals Unsupervised Learning with Generative Adversarial Network for Automatic Tire Defect Detection from X-ray Images

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6773
Author(s):  
Yilin Wang ◽  
Yulong Zhang ◽  
Li Zheng ◽  
Liedong Yin ◽  
Jinshui Chen ◽  
...  
Keyword(s):  
Defect Detection ◽  
Image Sensor ◽  
Reconstruction Method ◽  
Generative Adversarial Network ◽  
X Ray ◽  
Training Strategy ◽  
Tire Industry ◽  
Supervised Training ◽  
Adversarial Network ◽  
Unsupervised Approach

Automatic defect detection of tire has become an essential issue in the tire industry. However, it is challenging to inspect the inner structure of tire by surface detection. Therefore, an X-ray image sensor is used for tire defect inspection. At present, detection of defective tires is inefficient because tire factories commonly conduct detection by manually checking X-ray images. With the development of deep learning, supervised learning has been introduced to replace human resources. However, in actual industrial scenes, defective samples are rare in comparison to defect-free samples. The quantity of defective samples is insufficient for supervised models to extract features and identify nonconforming products from qualified ones. To address these problems, we propose an unsupervised approach, using no labeled defect samples for training. Moreover, we introduce an augmented reconstruction method and a self-supervised training strategy. The approach is based on the idea of reconstruction. In the training phase, only defect-free samples are used for training the model and updating memory items in the memory module, so the reproduced images in the test phase are bound to resemble defect-free images. The reconstruction residual is utilized to detect defects. The introduction of self-supervised training strategy further strengthens the reconstruction residual to improve detection performance. The proposed method is experimentally proved to be effective. The Area Under Curve (AUC) on a tire X-ray dataset reaches 0.873, so the proposed method is promising for application.

scholarly journals Defect Detection in Tire X-Ray Images Using Weighted Texture Dissimilarity

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Qiang Guo ◽  
Caiming Zhang ◽  
Hui Liu ◽  
Xiaofeng Zhang
Keyword(s):  
Defect Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Weighted Averaging ◽  
X Ray ◽  
Feature Vectors ◽  
Detection Algorithms ◽  
Quantitative Metrics ◽  
Feature Similarity ◽  
Anomaly Map

Automatic defect detection is an important and challenging problem in industrial quality inspection. This paper proposes an efficient defect detection method for tire quality assurance, which takes advantage of the feature similarity of tire images to capture the anomalies. The proposed detection algorithm mainly consists of three steps. Firstly, the local kernel regression descriptor is exploited to derive a set of feature vectors of an inspected tire image. These feature vectors are used to evaluate the feature dissimilarity of pixels. Next, the texture distortion degree of each pixel is estimated by weighted averaging of the dissimilarity between one pixel and its neighbors, which results in an anomaly map of the inspected image. Finally, the defects are located by segmenting this anomaly map with a simple thresholding process. Different from some existing detection algorithms that fail to work for tire tread images, the proposed detection algorithm works well not only for sidewall images but also for tread images. Experimental results demonstrate that the proposed algorithm can accurately locate the defects of tire images and outperforms the traditional defect detection algorithms in terms of various quantitative metrics.

Automatic measurement of SAED patterns from asbestos minerals

1988 ◽  
Vol 46 ◽  
pp. 846-847
Author(s):  
J. C. Russ ◽  
T. Taguchi ◽  
P. M. Peters ◽  
E. Chatfield ◽  
J. C. Russ ◽  
...  
Keyword(s):  
Diffraction Pattern ◽  
High Precision ◽  
Diffraction Data ◽  
Automatic Measurement ◽  
Automatic Method ◽  
Important Method ◽  
X Ray ◽  
Integrated Intensity ◽  
Asbestiform Minerals ◽  
True Center

Conventional SAD patterns as obtained in the TEM present difficulties for identification of materials such as asbestiform minerals, although diffraction data is considered to be an important method for making this purpose. The preferred orientation of the fibers and the spotty patterns that are obtained do not readily lend themselves to measurement of the integrated intensity values for each d-spacing, and even the d-spacings may be hard to determine precisely because the true center location for the broken rings requires estimation. We have implemented an automatic method for diffraction pattern measurement to overcome these problems. It automatically locates the center of patterns with high precision, measures the radius of each ring of spots in the pattern, and integrates the density of spots in that ring. The resulting spectrum of intensity vs. radius is then used just as a conventional X-ray diffractometer scan would be, to locate peaks and produce a list of d,I values suitable for search/match comparison to known or expected phases.

Development of a Pixel of Area Image Sensor in X-ray Detector

2018 ◽  
Vol 23 (6) ◽  
pp. 573-585
Author(s):  
D.A. Suponnikov ◽  
◽  
A.N. Putilin ◽  
E.A. Tatarinova ◽  
Z.G. Zhgunev ◽  
...  
Keyword(s):  
Image Sensor ◽  
X Ray
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