The Aircraft Skin Crack Inspection Based on Different-Source Sensors and Support Vector Machines

Wang, Congqing; Wang, Xianfeng; Zhou, Xin; Li, Zhiyu

doi:10.1007/s10921-016-0359-3

Cited by 17 publications

(12 citation statements)

References 14 publications

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“…Wang et al in [43] proposed a multi-class classification method, which applied support vector and machine (SVM) and data fusion to inspect aircraft skin crack. Shi et al proposed a CrackForest method to describe the crack feature with random structured forests, and the proposed the public CFD database with road crack images was very popular for scholars and researchers [44].…”

Section: Artificial Intelligencementioning

confidence: 99%

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

Fan

Chen

et al. 2020

Materials

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Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low- level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms.

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Section: Artificial Intelligencementioning

confidence: 99%

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

Fan

Chen

et al. 2020

Materials

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“…R. Mumtaz et al [32] proposed a new image processing technique using neural network for classifying crack and scratch on the body of the aircraft. Wang et al [54] developed a mobile platform for aircraft skin crack classification by fusing two di↵erent data modalities: CCD camera image and ultrasonic data. They designed features which they further used to train multi-class support vector machine in order to accomplish classification of cracks.…”

Section: Related Workmentioning

confidence: 99%

3D Point Cloud Analysis for Detection and Characterization of Defects on Airplane Exterior Surface

et al. 2017

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Three-dimensional surface defect inspection remains a challenging task. This paper describes a novel automatic vision-based inspection system that is capable of detecting and characterizing defects on an airplane exterior surface. By analyzing 3D data collected with a 3D scanner, our method aims to identify and extract the information about the undesired defects such as dents, protrusions or scratches based on local surface properties. Surface dents and protrusions are identified as the deviations from an ideal, smooth surface. Given an unorganized point cloud, we first smooth noisy data by using Moving Least Squares algorithm. The curvature and normal information are then estimated at every point in the input data. As a next step, Region Growing segmentation algorithm divides the point cloud into defective and non-defective regions using the local normal and curvature information. Further, the convex hull around each defective region is calculated in order to englobe the suspicious irregularity. Finally, we use our new technique to measure the dimension, depth, and orientation of the defects. We tested and validated our novel approach on real aircraft data obtained from an Airbus A320, for di↵erent types of defect. The accuracy of the system is evaluated by comparing the measurements of our approach with ground truth measurements obtained by a high-accuracy measuring device. The result shows that our work is robust, e↵ective and promising for industrial applications.

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“…Aircraft skins are one of the important components of the aerodynamic structure of aircraft, and they account for more than 50% of the total weight, so their health affects flight safety (Wu et al, 2019). The skin and skeleton of aircraft are connected by adhesives or rivets to bear and transfer aerodynamic load (Wang et al, 2016). It is easy for crack and fracture damage to occur under the action of cyclic load, thus reducing aircraft bearing capacity/ integrity and causing serious security risks (Al-Mukhtar, 2020).…”

Section: Introductionmentioning

confidence: 99%

Investigation the influence of miniaturized RFID tag sensor on coupling effect

Wang

Dong²,

Wang³

et al. 2021

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Purpose The skin and skeleton of aircraft are connected by adhesives or rivets to bear and transfer aerodynamic load. It is easy for crack and fracture damage to occur under the action of cyclic load, thus reducing aircraft bearing capacity/integrity and causing serious security risks. Therefore, it is particularly important that passive wireless radio frequency identification (RFID) sensors be used for the health monitoring of aircraft skin in its whole life cycle. This paper aims to investigate the influence of miniaturization on the coupling effect between RFID tag sensors. Design/methodology/approach Two groups of crack sensing systems based on RFID tags were designed. Gain and mutual impedance of sensor tags were analyzed via mode analysis. The reliability of crack detection of both sensing systems was compared using a preset experimental scheme. Findings Miniaturized antennas can reduce edge influence and the coupling effect. Gain and mutual impedance decrease with the increase in distance between dual tags. Backscatter power shows a decreasing trend and threshold power to activate tags in reader antenna increases. Results show that the miniaturization of size is more suitable for the application of multiple sensors. Originality/value By comparing two groups of sensing systems, the consistency of crack detection sensitivity is better when small tags are placed in parallel, which provides a theoretical basis for the application of small, passive and densely distributed crack sensors in the future.

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The Aircraft Skin Crack Inspection Based on Different-Source Sensors and Support Vector Machines

Cited by 17 publications

References 14 publications

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

3D Point Cloud Analysis for Detection and Characterization of Defects on Airplane Exterior Surface

Investigation the influence of miniaturized RFID tag sensor on coupling effect

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