Phase Fringe Pattern Filtering Method for Shearography Using Deep Learning
Wei Lin, Haihua Cui, Wei Zheng, Xinfang Zhou, Zhenlong Xu, and Wei Tian
As a noncontact high-precision optical full-field measurement method, shearography can be used for the nondestructive detection of internal defects in composite materials. However, the obtained phase fringe pattern contains a high amount of speckle noise that seriously affects the detection results and accuracy. Therefore, we propose a phase fringe-filtering method using an unsupervised image style conversion model (CycleGAN). Furthermore, the original noise phase fringe image obtained using shearography is converted into an ideal noiseless fringe image via network training to achieve noise filtering in the phase fringe pattern. The experimental results show that the proposed method achieves high-efficiency filtering for noise in areas where the stripe distribution is relatively sparse, with clear boundaries and significant contrast in filtered images. Additionally, the running time of the proposed method is better than that of the other methods (by approximately 30 ms), achieves high-quality filtering, meets the development demand of dynamic nondestructive testing, and provides a new idea for the noise filtering of phase fringe pattern.As a noncontact high-precision optical full-field measurement method, shearography can be used for the nondestructive detection of internal defects in composite materials. However, the obtained phase fringe pattern contains a high amount of speckle noise that seriously affects the detection results and accuracy. Therefore, we propose a phase fringe-filtering method using an unsupervised image style conversion model (CycleGAN). Furthermore, the original noise phase fringe image obtained using shearography is converted into an ideal noiseless fringe image via network training to achieve noise filtering in the phase fringe pattern. The experimental results show that the proposed method achieves high-efficiency filtering for noise in areas where the stripe distribution is relatively sparse, with clear boundaries and significant contrast in filtered images. Additionally, the running time of the proposed method is better than that of the other methods (by approximately 30 ms), achieves high-quality filtering, meets the development demand of dynamic nondestructive testing, and provides a new idea for the noise filtering of phase fringe pattern.
- Sep. 19, 2022
- Laser & Optoelectronics Progress
- Vol. 59, Issue 22, 2210009 (2022)
- DOI：10.3788/LOP202259.2210009
Hyperspectral Image Classification Based on Residual Generative Adversarial Network
Ming Chen, Xiangyun Xi, and Yang Wang
A hyperspectral image classification method based on residual generative adversarial network (GAN) is proposed to address the problems of high demand for labeled samples and high classification accuracy in the process of hyperspectral image classification. The method is based on GAN and includes: replacing the deconvolution layer network structure of the generator with an eight-layer residual network composed of an upsampling layer and a convolution layer to improve data generation ability; improving feature extraction ability, the discriminator's convolutional layer network structure is replaced with a thirty-four-layer residual convolutional network. The experiment compares the datasets from Indian Pines, Pavia University, and Salinas. The proposed method is compared to GAN, CAE-SVM, 2DCNN, 3DCNN, and ResNet. The results demonstrate that the proposed method improves overall classification accuracy, average classification accuracy, and Kappa coefficient significantly. Among them, the overall classification accuracy reached 98.84% on the Indian Pines dataset, which is 2.99 percentage points, 22.03 percentage points, 12.91 percentage points, 4.99 percentage points, and 1.79 percentage points higher than the comparison methods. In summary, adding a residual structure to the network improves information exchange between the shallow and deep networks, extracts deep features of the hyperspectral image, and improves hyperspectral image classification accuracy.A hyperspectral image classification method based on residual generative adversarial network (GAN) is proposed to address the problems of high demand for labeled samples and high classification accuracy in the process of hyperspectral image classification. The method is based on GAN and includes: replacing the deconvolution layer network structure of the generator with an eight-layer residual network composed of an upsampling layer and a convolution layer to improve data generation ability; improving feature extraction ability, the discriminator's convolutional layer network structure is replaced with a thirty-four-layer residual convolutional network. The experiment compares the datasets from Indian Pines, Pavia University, and Salinas. The proposed method is compared to GAN, CAE-SVM, 2DCNN, 3DCNN, and ResNet. The results demonstrate that the proposed method improves overall classification accuracy, average classification accuracy, and Kappa coefficient significantly. Among them, the overall classification accuracy reached 98.84% on the Indian Pines dataset, which is 2.99 percentage points, 22.03 percentage points, 12.91 percentage points, 4.99 percentage points, and 1.79 percentage points higher than the comparison methods. In summary, adding a residual structure to the network improves information exchange between the shallow and deep networks, extracts deep features of the hyperspectral image, and improves hyperspectral image classification accuracy.
- Sep. 23, 2022
- Laser & Optoelectronics Progress
- Vol. 59, Issue 22, 2210008 (2022)
- DOI：10.3788/LOP202259.2210008
Multi-Focus Image Fusion Method Based on Double-Scale Decomposition and Random Walk
Xiaomiao Li, Yanchun Yang, Jianwu Dang, and Yangping Wang
This paper proposes a multi-focus image fusion method based on double-scale decomposition and random walk to smooth the edge region and avoid artifacts at the edge junction. The source images are first decomposed into large-scale and small-scale focus images using a Gaussian filter, and the edges of the decomposed large-scale and small-scale focus images are smoothed using various guiding filters. Then, the large-scale and small-scale focus maps are used as the marker nodes of the random walk algorithm, the initial decision map is obtained using the fusion algorithm, and the guided filter is used to optimize the decision map again. Finally, the source images are reconstructed using the decision graphs to produce the final fused image. The results of the experiments show that our method can effectively obtain the focus information in the source images while retaining the edge texture and detailed information of the focus area. It outperformed the competition in both subjective and objective evaluation indicators.This paper proposes a multi-focus image fusion method based on double-scale decomposition and random walk to smooth the edge region and avoid artifacts at the edge junction. The source images are first decomposed into large-scale and small-scale focus images using a Gaussian filter, and the edges of the decomposed large-scale and small-scale focus images are smoothed using various guiding filters. Then, the large-scale and small-scale focus maps are used as the marker nodes of the random walk algorithm, the initial decision map is obtained using the fusion algorithm, and the guided filter is used to optimize the decision map again. Finally, the source images are reconstructed using the decision graphs to produce the final fused image. The results of the experiments show that our method can effectively obtain the focus information in the source images while retaining the edge texture and detailed information of the focus area. It outperformed the competition in both subjective and objective evaluation indicators.
- Sep. 23, 2022
- Laser & Optoelectronics Progress
- Vol. 59, Issue 22, 2210011 (2022)
- DOI：10.3788/LOP202259.2210011
Algorithm for Plug Seedling Classification Based on Improved Attention Mechanism Residual Network
Cong Wu, Zhiqiang Guo, and Jie Yang
A residual network based on dual-channel attention mechanism is designed to address the difficulty in extracting single and segmented seedling dataset images from the channel attention mechanism feature of the SENet network, which integrates the channel attention mechanism and spatial attention. The mechanism module can obtain the channel and spatial dimension feature weights simultaneously to enhance the feature learning ability of the network. To address the problem of missing the target in the segmented sample data, a random erasure method is proposed. Experiments on the self-made plug seedling Plant_seed dataset demonstrate that the improved network ResNet34+CBAM_basic_conv, which introduces the attention mechanism module between the ResNet34 network residual module and the conv*_x module, reaches the optimal accuracy of 93.8%. The error rate of the model classification drops after some images in the dataset are randomly erased, demonstrating the excellent performance of the proposed method.A residual network based on dual-channel attention mechanism is designed to address the difficulty in extracting single and segmented seedling dataset images from the channel attention mechanism feature of the SENet network, which integrates the channel attention mechanism and spatial attention. The mechanism module can obtain the channel and spatial dimension feature weights simultaneously to enhance the feature learning ability of the network. To address the problem of missing the target in the segmented sample data, a random erasure method is proposed. Experiments on the self-made plug seedling Plant_seed dataset demonstrate that the improved network ResNet34+CBAM_basic_conv, which introduces the attention mechanism module between the ResNet34 network residual module and the conv*_x module, reaches the optimal accuracy of 93.8%. The error rate of the model classification drops after some images in the dataset are randomly erased, demonstrating the excellent performance of the proposed method.
- Sep. 23, 2022
- Laser & Optoelectronics Progress
- Vol. 59, Issue 22, 2210002 (2022)
- DOI：10.3788/LOP202259.2210002
Application of Deep Learning in Intravascular Optical Coherence Tomography
Zheng Sun, and Shuyan Wang
Intravascular optical coherence tomography (IVOCT) is a minimally invasive imaging model that currently has the highest resolution. It is capable of providing information of the vascular lumen morphology and near-microscopic structures of the vessel wall. For each pullback of the target vessel, hundreds or thousands of B-scan images are obtained in routine clinical applications. Manual image analysis is time-consuming and laborious, and the findings depend on the operators' professional ability in some sense. Recently, as deep learning technology has continuously made significant breakthroughs in the medical imaging field, it has also been used in the computer-aided automated analysis of IVOCT images. This study outlines the applications of deep learning in IVOCT, primarily involving image segmentation, tissue characterization, plaque classification, and object detection. The benefits and limitations of the existing approaches are discussed, and the future possible development is described.Intravascular optical coherence tomography (IVOCT) is a minimally invasive imaging model that currently has the highest resolution. It is capable of providing information of the vascular lumen morphology and near-microscopic structures of the vessel wall. For each pullback of the target vessel, hundreds or thousands of B-scan images are obtained in routine clinical applications. Manual image analysis is time-consuming and laborious, and the findings depend on the operators' professional ability in some sense. Recently, as deep learning technology has continuously made significant breakthroughs in the medical imaging field, it has also been used in the computer-aided automated analysis of IVOCT images. This study outlines the applications of deep learning in IVOCT, primarily involving image segmentation, tissue characterization, plaque classification, and object detection. The benefits and limitations of the existing approaches are discussed, and the future possible development is described.
- Sep. 23, 2022
- Laser & Optoelectronics Progress
- Vol. 59, Issue 22, 2200002 (2022)
- DOI：10.3788/LOP202259.2200002
Panicle Segmentation and Characteristics Analysis of Rice During Filling Stage Based on Neural Architecture Search
Jiawei Zhu, Zhaohui Jiang, Shilan Hong, Huimin Ma, Jianpeng Xu, and Maosheng Jin
The grain filling stage is a critical growth phase of rice. To segment the panicle accurately during filling stage and explore the relationship between its characteristics and plant maturation, a method of segmentation and characteristics analysis is proposed based on neural architecture search (NAS). Based on the DeepLabV3Plus network model, the backbone network is automatically designed using NAS, and the semantic segmentation network Rice-DeepLab is built by modifying atrous spatial pyramid pooling (ASPP). The area ratios, dispersion, average curvature, and color characteristics of the panicles of four rice varieties are calculated and analyzed after segmentation by Rice-DeepLab. The experimental results show that the improved Rice-DeepLab network has a mean intersection over union (mIoU) of 85.74% and accuracy (Acc) of 92.61%, which is 6.5% and 2.97% higher than that of the original model, respectively. According to the panicles' area ratios, dispersion, average curvature, and color characteristics recorded in the image, it can be roughly distinguished whether the panicles are sparse or dense, whether grain filling is complete, and whether the color is green, golden, or gray. This study suggests that field cameras can be easily used to monitor rice in the filling stage preliminarily to estimate maturation and crop size by panicle segmentation and characteristics analysis, thus providing support for field management.The grain filling stage is a critical growth phase of rice. To segment the panicle accurately during filling stage and explore the relationship between its characteristics and plant maturation, a method of segmentation and characteristics analysis is proposed based on neural architecture search (NAS). Based on the DeepLabV3Plus network model, the backbone network is automatically designed using NAS, and the semantic segmentation network Rice-DeepLab is built by modifying atrous spatial pyramid pooling (ASPP). The area ratios, dispersion, average curvature, and color characteristics of the panicles of four rice varieties are calculated and analyzed after segmentation by Rice-DeepLab. The experimental results show that the improved Rice-DeepLab network has a mean intersection over union (mIoU) of 85.74% and accuracy (Acc) of 92.61%, which is 6.5% and 2.97% higher than that of the original model, respectively. According to the panicles' area ratios, dispersion, average curvature, and color characteristics recorded in the image, it can be roughly distinguished whether the panicles are sparse or dense, whether grain filling is complete, and whether the color is green, golden, or gray. This study suggests that field cameras can be easily used to monitor rice in the filling stage preliminarily to estimate maturation and crop size by panicle segmentation and characteristics analysis, thus providing support for field management.
- Sep. 19, 2022
- Laser & Optoelectronics Progress
- Vol. 59, Issue 22, 2210012 (2022)
- DOI：10.3788/LOP202259.2210012
Three-Dimensional Object Detection in Substation Operation Scene Based on Attention Mechanism
Wei Gao, Boyang He, Ting Zhang, Meiqing Guo, Jun Liu, Huimin Wang, and Xingzhong Zhang
The perception of the spatial distance between operators and dangerous equipment is a basic safety management and control task issue in a substation scene. With the advancement of lidar and three-dimensional (3D) vision theory, 3D point cloud target detection can provide necessary technical assistance for downstream spatial distance measurement tasks. Aiming at the problem of inaccurate target detection caused by factors such as complex background and equipment occlusion in the substation scene, based on the PointNet++ model, an improved attention module is introduced in the local feature extraction stage, and a 3D object detection network PointNet suitable for substation operation scene is proposed. First, the network undergoes a two-level local feature extraction to obtain fine-grained features in each local area, then encodes all local features into feature vectors using a mini-pointnet to obtain global features, and finally passes through the fully connected layer to predict the results. Considering the large gap between the number of front and background points in the cloud data of substation sites, this study calculates the classification loss using focal loss to make the network pay more attention to the feature information of the front points. Experiments on the self-built dataset show that the PowerNet has a mean average precision (mAP) value of 0.735, which is greater than previous models and can be directly applied to downstream security management and control tasks.The perception of the spatial distance between operators and dangerous equipment is a basic safety management and control task issue in a substation scene. With the advancement of lidar and three-dimensional (3D) vision theory, 3D point cloud target detection can provide necessary technical assistance for downstream spatial distance measurement tasks. Aiming at the problem of inaccurate target detection caused by factors such as complex background and equipment occlusion in the substation scene, based on the PointNet++ model, an improved attention module is introduced in the local feature extraction stage, and a 3D object detection network PointNet suitable for substation operation scene is proposed. First, the network undergoes a two-level local feature extraction to obtain fine-grained features in each local area, then encodes all local features into feature vectors using a mini-pointnet to obtain global features, and finally passes through the fully connected layer to predict the results. Considering the large gap between the number of front and background points in the cloud data of substation sites, this study calculates the classification loss using focal loss to make the network pay more attention to the feature information of the front points. Experiments on the self-built dataset show that the PowerNet has a mean average precision (mAP) value of 0.735, which is greater than previous models and can be directly applied to downstream security management and control tasks.
- Sep. 23, 2022
- Laser & Optoelectronics Progress
- Vol. 59, Issue 22, 2210010 (2022)
- DOI：10.3788/LOP202259.2210010
Infrared Thermal Imaging Detection of Surface Cracks in External Insulation Layer of Building Exterior Wall
Jiayi Wang, and Zhongxing Duan
The external insulation layer of a building's exterior wall is widely used for energy conservation in buildings. An infrared imaging detection method based on a three-dimensional heat transfer model for the surface cracks of the external insulation layer is proposed to effectively detect the quality problems of the external insulation layer. First, with the help of infrared thermal imaging technology, an experimental platform for surface crack detection of the external insulation layer of building exterior wall was built to detect the surface crack of the external insulation layer. Then, using ANSYS software, the three-dimensional infrared thermal imaging detection model for the surface cracks of the external insulation layer of the building exterior wall was established, the model's feasibility was verified, and the effects of crack size and ambient temperature on the detection effect were simulated and calculated.The results show that the experimental ambient temperature and crack size have the greatest influence on the temperature difference between the crack and non-crack areas of the external insulation layer. When the ambient temperature remains constant, the crack width and thickness grow, as does the temperature difference. With an increase in ambient temperature, the temperature difference also increases gradually. When the ambient temperature is below 10 ℃, the temperature difference changes gently; when the ambient temperature exceeds 10 ℃, the rate of temperature difference growth increases gradually with increasing ambient temperature.The external insulation layer of a building's exterior wall is widely used for energy conservation in buildings. An infrared imaging detection method based on a three-dimensional heat transfer model for the surface cracks of the external insulation layer is proposed to effectively detect the quality problems of the external insulation layer. First, with the help of infrared thermal imaging technology, an experimental platform for surface crack detection of the external insulation layer of building exterior wall was built to detect the surface crack of the external insulation layer. Then, using ANSYS software, the three-dimensional infrared thermal imaging detection model for the surface cracks of the external insulation layer of the building exterior wall was established, the model's feasibility was verified, and the effects of crack size and ambient temperature on the detection effect were simulated and calculated.The results show that the experimental ambient temperature and crack size have the greatest influence on the temperature difference between the crack and non-crack areas of the external insulation layer. When the ambient temperature remains constant, the crack width and thickness grow, as does the temperature difference. With an increase in ambient temperature, the temperature difference also increases gradually. When the ambient temperature is below 10 ℃, the temperature difference changes gently; when the ambient temperature exceeds 10 ℃, the rate of temperature difference growth increases gradually with increasing ambient temperature.
- Sep. 19, 2022
- Laser & Optoelectronics Progress
- Vol. 59, Issue 22, 2204001 (2022)
- DOI：10.3788/LOP202259.2204001
Rapid Restoration of Turbulent Degraded Images Based on Bidirectional Multi-Scale Feature Fusion
Yiming Guo, Xiaoqing Wu, Changdong Su, Shitai Zhang, Cuicui Bi, and Zhiwei Tao
This study proposes a generative adversarial network (GAN) based on bidirectional multi-scale feature fusion to reconstruct target celestial images captured by various ground-based telescopes, which are influenced by atmospheric turbulence. This approach first constructs a dataset for network training by convolving a long-exposure atmospheric turbulence degradation model with clear images and then validates the network's performance on a simulated turbulence image dataset. Furthermore, images of the International Space Station collected by the Munin ground-based telescope (Cassegrain-type telescope) that were influenced by atmospheric turbulence are included in this study. These images were sent to the proposed neural network model for testing. Different image restoration assessment shows that the proposed network has a good real-time performance and can produce restoration results within 0.5 s, which is more than 10 times faster than standard nonneural network restoration approaches; the peak signal to noise ratio (PSNR) is improved by 2 dB?3 dB, and structural similarity (SSIM) is enhanced by 9.3%. Simultaneously, the proposed network has a pretty good restoration impact on degraded images that are influenced by real turbulence.This study proposes a generative adversarial network (GAN) based on bidirectional multi-scale feature fusion to reconstruct target celestial images captured by various ground-based telescopes, which are influenced by atmospheric turbulence. This approach first constructs a dataset for network training by convolving a long-exposure atmospheric turbulence degradation model with clear images and then validates the network's performance on a simulated turbulence image dataset. Furthermore, images of the International Space Station collected by the Munin ground-based telescope (Cassegrain-type telescope) that were influenced by atmospheric turbulence are included in this study. These images were sent to the proposed neural network model for testing. Different image restoration assessment shows that the proposed network has a good real-time performance and can produce restoration results within 0.5 s, which is more than 10 times faster than standard nonneural network restoration approaches; the peak signal to noise ratio (PSNR) is improved by 2 dB?3 dB, and structural similarity (SSIM) is enhanced by 9.3%. Simultaneously, the proposed network has a pretty good restoration impact on degraded images that are influenced by real turbulence.
- Sep. 19, 2022
- Laser & Optoelectronics Progress
- Vol. 59, Issue 22, 2201001 (2022)
- DOI：10.3788/LOP202259.2201001
Obstacle Detection for a Pipeline Point Cloud Based on Time Series and Neighborhood Analysis
Shiyu Lin, Xuejiao Yan, Zhe Xie, Hongwen Fu, Song Jiang, Hongzhi Jiang, Xudong Li, and Huijie Zhao
Employing a robot to inspect the inner surface of the pipeline periodically is crucial to guarantee that the pipeline runs safely and reliably. Limited by the robot size and power, small three-dimensional measurement sensors with lower accuracy are frequently used with the robot to obtain environmental and navigation information. However, the quality of the pipeline point cloud acquired using such a sensor is substandard, making it challenging to reliably detect obstacles. Therefore, a point cloud processing approach according to time series and neighborhood analysis is proposed, which employs time and spatial distribution characteristics of obstacle point clouds and noise point clouds to remove noise and finally detects the obstacles by fitting the pipeline inner wall point clouds. The experiments reveal that the detection accuracy improves by 30 percentage points and the processing time is less than 1 s, meeting the requirements of the pipeline inspection robot.Employing a robot to inspect the inner surface of the pipeline periodically is crucial to guarantee that the pipeline runs safely and reliably. Limited by the robot size and power, small three-dimensional measurement sensors with lower accuracy are frequently used with the robot to obtain environmental and navigation information. However, the quality of the pipeline point cloud acquired using such a sensor is substandard, making it challenging to reliably detect obstacles. Therefore, a point cloud processing approach according to time series and neighborhood analysis is proposed, which employs time and spatial distribution characteristics of obstacle point clouds and noise point clouds to remove noise and finally detects the obstacles by fitting the pipeline inner wall point clouds. The experiments reveal that the detection accuracy improves by 30 percentage points and the processing time is less than 1 s, meeting the requirements of the pipeline inspection robot.
- Sep. 23, 2022
- Laser & Optoelectronics Progress
- Vol. 59, Issue 22, 2210007 (2022)
- DOI：10.3788/LOP202259.2210007
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