- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Noncontact detection of concrete flaws by neural network classification of laser doppler vibrometer signals
摘要： This study aimed to develop a non-contact and high-speed damage detection technology for use on concrete structures. A laser Doppler vibrometer was used to obtain the vibrations of a concrete structure at a high signal-to-noise ratio. The observed vibration data were transformed into frequency spectra by Fourier transform. Using the simulation by the finite element method, it was predicted that the characteristic spectrum appeared in the low frequency region for the cracked part. However, the experimental results did not show such a difference clearly. In contrast, in the high-frequency region of the experimental data, a spectrum peculiar to the cracked part tended to appear. Nonetheless, the difference was so small that it was often buried by variations in hammering strength. Therefore, it was difficult to manually determine the signal of the cracked part. Machine learning using a convolutional neural network was carried out in order to judge the location and dimensions of a cracked part with high accuracy. As a result, cracks in the concrete were detected with a high accuracy of more than 90%.
关键词： convolutional neural network,laser doppler vibrometer,concrete,non-destructive inspection
更新于2025-09-23 15:21:01
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Large-scale Multi-class Image-based Cell Classification with Deep Learning
摘要： Recent advances in ultra-high-throughput optical microscopy have enabled a new generation of cell classification methodologies using image-based cell phenotypes alone. In contrast to the current single-cell analysis techniques that rely solely on slow and costly genetic/epigenetic analyses, these image-based classification methods allow morphological profiling and screening of thousands or even millions of single cells at a fraction of the cost. Furthermore, they have demonstrated the statistical significance required for understanding the role of cell heterogeneity in diverse biological applications, ranging from cancer screening to drug candidate identification/validation processes. This work examines the efficacies and opportunities presented by machine learning algorithms in processing large-scale datasets with millions of label-free cell images. An automatic single-cell classification framework using a convolutional neural network (CNN) has been developed. A comparative analysis of its efficiency in classifying large datasets against conventional k-nearest neighbors (kNN) and support vector machine (SVM) based methods is also presented. Experiments have shown that (i) our proposed framework can identify multiple types of cells with over 99 % accuracy based on label-free bright-field images efficiently; (ii) CNN-based models perform well and relatively stable against changes in data volume compared with kNN and SVM.
关键词： Convolutional neural network,Bright field imaging,Multiclass classification,Cell classification
更新于2025-09-23 15:21:01
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PhaseNet: A Deep Convolutional Neural Network for 2D Phase Unwrapping
摘要： Phase unwrapping is a crucial signal processing problem in several applications that aims to restore original phase from the wrapped phase. In this letter, we propose a novel framework for unwrapping the phase using deep fully convolutional neural network termed as PhaseNet. We reformulate the problem definition of directly obtaining continuous original phase as obtaining the wrap-count (integer jump of 2π) at each pixel by semantic segmentation, and this is accomplished through a suitable deep learning framework. The proposed architecture consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The relationship between the absolute phase and the wrap-count is leveraged in generating abundant simulated data of several random shapes. This deliberates the network on learning continuity in wrapped phase maps rather than specific patterns in the training data. We compare the proposed framework with the widely adapted quality-guided phase unwrapping algorithm and also with the well known MATLAB’s unwrap function for varying noise levels. The proposed framework is found to be robust to noise and computationally fast. The results obtained highlight that Deep Convolutional Neural Network (DCNN) can indeed be effectively applied for phase unwrapping, and the proposed framework will hopefully pave the way for the development of a new set of deep learning based phase unwrapping methods.
关键词： Encoder,Decoder,Phase Unwrapping,Deep Convolutional Neural Network,Semantic Segmentation
更新于2025-09-23 15:21:01
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PET Image Denoising Using a Deep Neural Network Through Fine Tuning
摘要： Positron emission tomography (PET) is a functional imaging modality widely used in clinical diagnosis. In this work, we trained a deep convolutional neural network (CNN) to improve PET image quality. Perceptual loss based on features derived from a pre-trained VGG network, instead of the conventional mean squared error, was employed as the training loss function to preserve image details. As the number of real patient data set for training is limited, we propose to pre-train the network using simulation data and fine-tune the last few layers of the network using real data sets. Results from simulation, real brain and lung data sets show that the proposed method is more effective in removing noise than the traditional Gaussian filtering method.
关键词： Positron emission tomography,fine-tuning,convolutional neural network,perceptual loss,image denoising
更新于2025-09-23 15:21:01
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Method for Mapping Rice Fields in Complex Landscape Areas Based on Pre-Trained Convolutional Neural Network from HJ-1 A/B Data
摘要： Accurate and timely information about rice planting areas is essential for crop yield estimation, global climate change and agricultural resource management. In this study, we present a novel pixel-level classi?cation approach that uses convolutional neural network (CNN) model to extract the features of enhanced vegetation index (EVI) time series curve for classi?cation. The goal is to explore the practicability of deep learning techniques for rice recognition in complex landscape regions, where rice is easily confused with the surroundings, by using mid-resolution remote sensing images. A transfer learning strategy is utilized to ?ne tune a pre-trained CNN model and obtain the temporal features of the EVI curve. Support vector machine (SVM), a traditional machine learning approach, is also implemented in the experiment. Finally, we evaluate the accuracy of the two models. Results show that our model performs better than SVM, with the overall accuracies being 93.60% and 91.05%, respectively. Therefore, this technique is appropriate for estimating rice planting areas in southern China on the basis of a pre-trained CNN model by using time series data. And more opportunity and potential can be found for crop classi?cation by remote sensing and deep learning technique in the future study.
关键词： mapping rice ?elds,convolutional neural network,time series of vegetation index,complex landscape,transfer learning
更新于2025-09-23 15:21:01
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Convolutional Neural Network for Blind Mesh Visual Quality Assessment Using 3D Visual Saliency
摘要： In this work, we propose a convolutional neural network (CNN) framework to estimate the perceived visual quality of 3D meshes without having access to the reference. The proposed CNN architecture is fed by small patches selected carefully according to their level of saliency. To do so, the visual saliency of the 3D mesh is computed, then we render 2D projections from the 3D mesh and its corresponding 3D saliency map. Afterward, the obtained views are split to obtain 2D small patches that pass through a saliency filter to select the most relevant patches. Experiments are conducted on two MVQ assessment databases, and the results show that the trained CNN achieves good rates in terms of correlation with human judgment.
关键词： blind mesh visual quality assessment,Convolutional neural network (CNN),mesh visual saliency
更新于2025-09-23 15:21:01
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Scene Recognition with Convolutional Residual Features via Deep Forest
摘要： Convolutional Neural Networks (CNNs) have made remarkable progress on image classification and other relative computer vision filed, which need large-scale data for training. In this paper, a method named DFCRF (Deep Forest with Convolutional Residual Features) is proposed. It is based on the gcForest proposed by Zhou and Feng. And we use a recent released AI Challenger dataset, containing around only 50,000 images mainly captured in China. Different from utilizing only CNNs, we use convolutional residual features for further recognition, followed by gradient-based XGBoost and cascade deep forest. Then, we conduct extensive experiments on the AI Challenger dataset and reconstructed Places2 dataset to show the effectiveness of our method.
关键词： deep forest,scene recognition,AI challenger,convolutional neural network
更新于2025-09-23 15:21:01
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A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
摘要： Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At ?rst, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation (S) and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (I). At the end, we back into the original RGB space to get the ?nal improved image. Experimental results show that the proposed algorithm not only enhances the image brightness and contrast signi?cantly, but also avoids color distortion and over-enhancement in comparison with some other state-of-the-art research papers. So, it effectively improves the quality of sensor images.
关键词： convolutional neural network,Retinex model,image enhancement,color model,batch normalization,feature learning
更新于2025-09-23 15:21:01
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Visual Tree Convolutional Neural Network in Image Classification
摘要： In image classification, Convolutional Neural Network (CNN) models have achieved high performance with the rapid development in deep learning. However, some categories in the image datasets are more difficult to distinguished than others. Improving the classification accuracy on these confused categories is benefit to the overall performance. In this paper, we build a Confusion Visual Tree (CVT) based on the confused semantic level information to identify the confused categories. With the information provided by the CVT, we can lead the CNN training procedure to pay more attention on these confused categories. Therefore, we propose Visual Tree Convolutional Neural Networks (VT-CNN) based on the original deep CNN embedded with our CVT. We evaluate our VT-CNN model on the benchmark datasets CIFAR-10 and CIFAR-100. In our experiments, we build up 3 different VT-CNN models and they obtain improvement over their based CNN models by 1.36%, 0.89% and 0.64%, respectively.
关键词： Image Classification,Convolutional Neural Network,Confusion Visual Tree,Deep Learning
更新于2025-09-23 15:21:01
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Semi-supervised convolutional neural networks with label propagation for image classification
摘要： Over the past several years, deep learning has achieved promising performance in many visual tasks, e.g., face verification and object classification. However, a limited number of labeled training samples existing in practical applications is still a huge bottleneck for achieving a satisfactory performance. In this paper, we integrate class estimation of unlabeled training data with deep learning model which generates a novel semi-supervised convolutional neural network (SSCNN) trained by both the labeled training data and unlabeled data. In the framework of SSCNN, the deep convolution feature extraction and the class estimation of the unlabeled data are jointly learned. Specifically, deep convolution features are learned from the labeled training data and unlabeled data with confident class estimation. After the deep features are obtained, the label propagation algorithm is utilized to estimate the identities of unlabeled training samples. The alternative optimization of SSCNN makes the class estimation of unlabeled data more and more accurate due to the learned CNN feature more and more discriminative. We compared the proposed SSCNN with some representative semi-supervised learning approaches on MINIST and Cifar-10 databases. Extensive experiments on landmark databases show the effectiveness of our semi-supervised deep learning framework.
关键词： convolutional neural network,semi-supervised learning,label propagation
更新于2025-09-23 15:21:01