Crack Detection Method of Sleeper Based on Cascade Convolutional Neural Network

Li, Liming; Zheng, Shibao; Wang, Chenxi; Zhao, Shenghui; Chai, Xiaodong; Peng, Lele; Tong, Qianqian; Ji, Wang

doi:10.1155/2022/7851562

Cited by 4 publications

(4 citation statements)

References 42 publications

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“…Deep learning [14][15][16][17][18] has recently been successful in the image recognition and target detection fields. Universally, scholars apply deep learning to achieve ultrasonic Lamb wave imaging.…”

Section: Introductionmentioning

confidence: 99%

Super-resolution reconstruction of ultrasonic Lamb wave TFM image via deep learning

Zhang¹,

chai²,

Zhu³

et al. 2023

Meas. Sci. Technol.

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Under the same detection frequency and depth, when the center spacing of multiple defects is less than the resolution threshold determined by the Rayleigh criterion, it is challenging to achieve super-resolution imaging of multiple defects using the ultrasonic Total Focusing Method (TFM). A multilevel deep learning network is proposed as a super-resolution reconstruction method for ultrasonic Lamb wave TFM images . The first-level network is a detection network that uses a Resnet50 with more convolutional layers to improve the linear expression capabilities of the neural network. It introduces residual structure to solve low accuracy issues of multi-convolutional layers so that the Resnet50 can accurately detect defects from TFM images. The second-level network is a super-resolution reconstruction network that uses a Deeplab v3+ with a dilated convolutional layer. It controls the receptive field without changing the feature map size of the TFM image. With this model, a super-resolution reconstruction of multiple defects with a center spacing less than the resolution threshold is realized by extracting the detailed features of defects in the TFM image. Experimental results show that when the defect center spacing is greater than the resolution threshold determined by the Rayleigh criterion, the super-resolution reconstruction method improves the calculation accuracy of the defect center spacing by 4.7% and the calculation accuracy of the defect area by 93.7% compared with TFM. When the defect spacing is less than the resolution threshold, the method can still identify and accurately calculate the center spacing of multiple defects.

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Section: Introductionmentioning

confidence: 99%

Super-resolution reconstruction of ultrasonic Lamb wave TFM image via deep learning

Zhang¹,

chai²,

Zhu³

et al. 2023

Meas. Sci. Technol.

Self Cite

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“…Later on, Cha et al [12] and Eisenbach et al [13] also performed patch-based classification, which can only identify the presence or absence of cracks in a corresponding image patch. Researchers also utilized another deep learning scheme called object detection for localizing the cracks, along with identifying them in an image [14,15]. However, these models can only classify and localize the cracks in a concrete structure instead of detecting cracks at a pixel level.…”

Section: Introductionmentioning

confidence: 99%

Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System

et al. 2023

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Crack inspection is important to monitor the structural health of pavement structures and make the repair process easier. Currently, pavement crack inspection is conducted manually, which is inefficient and costly at the same time. To solve the problem, this work has developed a robotic system for automated data collection and analysis in real-time. The robotic system navigates the pavement and collects visual images from the surface. A deep-learning-based semantic segmentation framework named RCDNet was proposed. The RCDNet was implemented on the onboard computer of the robot to identify cracks from the visual images. The encoder-decoder architecture was utilized as the base framework of the proposed RCDNet. The RCDNet comprises a dual-channel encoder and a decoder module. The encoder and decoder parts contain a context-embedded channel attention (CECA) module and a global attention module (GAM), respectively. Simulation results show that the deep learning model obtained 96.29% accuracy for predicting the images. The proposed robotic system was tested in both indoor and outdoor environments. The robot was observed to complete the inspection of a 3 m × 2 m grid within 10 min and a 2.5 m × 1 m grid within 6 min. This outcome shows that the proposed robotic method can drastically reduce the time of manual inspection. Furthermore, a severity map was generated using the visual image results. This map highlights areas that require greater attention for repair in the test grid.

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“…Y Zhang et al [ 7 ] used the YOLO v3 algorithm as a base and was able to detect the cracks efficiently by using MobileNet for transfer learning and the convolutional block attention model. The authors of [ 8 ] carried out research on various CNN-based algorithms, and found that MobileNet yielded the best accuracy for a masonry dataset. J. K. Chow et al [ 9 ] carried out crack detection on concrete images using a convolutional autoencoder and decoders.…”

Section: Introductionmentioning

confidence: 99%

Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis

Paramanandham

Rajendiran²,

et al. 2023

Sensors

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This research article is aimed at improving the efficiency of a computer vision system that uses image processing for detecting cracks. Images are prone to noise when captured using drones or under various lighting conditions. To analyze this, the images were gathered under various conditions. To address the noise issue and to classify the cracks based on the severity level, a novel technique is proposed using a pixel-intensity resemblance measurement (PIRM) rule. Using PIRM, the noisy images and noiseless images were classified. Then, the noise was filtered using a median filter. The cracks were detected using VGG-16, ResNet-50 and InceptionResNet-V2 models. Once the crack was detected, the images were then segregated using a crack risk-analysis algorithm. Based on the severity level of the crack, an alert can be given to the authorized person to take the necessary action to avoid major accidents. The proposed technique achieved a 6% improvement without PIRM and a 10% improvement with the PIRM rule for the VGG-16 model. Similarly, it showed 3 and 10% for ResNet-50, 2 and 3% for Inception ResNet and a 9 and 10% increment for the Xception model. When the images were corrupted from a single noise alone, 95.6% accuracy was achieved using the ResNet-50 model for Gaussian noise, 99.65% accuracy was achieved through Inception ResNet-v2 for Poisson noise, and 99.95% accuracy was achieved by the Xception model for speckle noise.

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Crack Detection Method of Sleeper Based on Cascade Convolutional Neural Network

Cited by 4 publications

References 42 publications

Super-resolution reconstruction of ultrasonic Lamb wave TFM image via deep learning

Super-resolution reconstruction of ultrasonic Lamb wave TFM image via deep learning

Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System

Pixel Intensity Resemblance Measurement and Deep Learning Based Computer Vision Model for Crack Detection and Analysis

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