Video and image sources are frequently applied in the area of defect inspection in industrial community. For the recognition and classification of sewer defects, a significant number of videos and ...images of sewers are collected. These data are then checked by human and some traditional methods to recognize and classify the sewer defects, which is inefficient and error-prone. Previously developed features like SIFT are unable to comprehensively represent such defects. Therefore, feature representation is especially important for defect autoclassification. In this paper, we study the automatic extraction of feature representation for sewer defects via deep learning. Moreover, a complete automatic system for classifying sewer defects is proposed built on a two-level hierarchical deep convolutional neural network, which shows high performance with respect to classification accuracy. The proposed network is trained on a novel data set with over 40 000 sewer images. The system has been successfully applied in the practical production, confirming its robustness and feasibility to real-world applications. The source code and trained model are available at the project website. 1
This article investigates the problem of specific emitter identification (SEI), i.e., radar emitter fingerprint or individual emitter identification, which first measures the emitter-specific ...differences caused by radar's nonlinearities, e.g., mixer, power amplifiers, transmitter, and then makes a decision. In this article, the SEI problem is considered in the single-modal, dual-modal, and multimodal scenarios, respectively. First, a multimodal subspace interactive mutual unit is proposed to perform information interaction between radar signal and its multiple transformations. Based on this, a data-driven multimodal subspace interactive mutual network is then built to solve the SEI problem. Extensive experiment results demonstrate that the proposed algorithm achieves superior identification performance on the airplane measured data.
The fusion of high spatial resolution panchromatic (PAN) data with simultaneously acquired multispectral (MS) data with the lower spatial resolution is a hot topic, which is often called ...pansharpening. In this article, we exploit the combination of machine learning techniques and fusion schemes introduced to address the pansharpening problem. In particular, deep convolutional neural networks (DCNNs) are proposed to solve this issue. The latter is combined first with the traditional component substitution and multiresolution analysis fusion schemes in order to estimate the nonlinear injection models that rule the combination of the upsampled low-resolution MS image with the extracted details exploiting the two philosophies. Furthermore, inspired by these two approaches, we also developed another DCNN for pansharpening. This is fed by the direct difference between the PAN image and the upsampled low-resolution MS image. Extensive experiments conducted both at reduced and full resolutions demonstrate that this latter convolutional neural network outperforms both the other detail injection-based proposals and several state-of-the-art pansharpening methods.
Road traffic is an important component of the national economy and social life. Promoting intelligent and Informa ionization construction in the field of road traffic is conducive to the construction ...of smart cities and the formulation of macro strategies and construction plans for urban traffic development. Aiming at the shortcomings of the current road traffic system, this article, on the basis of combining convolution neural network, situational awareness technology, database and other technologies, takes the road traffic situational awareness system as the research object, and analyzes the information collection, processing, and analysis process of road traffic situational awareness system. Convolutional neural networks (CNN), region-CNN (R-CNN), fast R-CNN, and faster R-CNN are used for vehicle class classification and location identification in road image big data. The deep convolutional neural network model based on road traffic image big data was further established, and the system requirements analysis and system framework design and implementation were carried out. Through the analysis and trial of actual cases, the results show the application effect of the realized road traffic situational awareness system, which provides a scientific reference and basis for the establishment of modern intelligent transportation system.
Data-driven artificial intelligence methods, especially convolutional neural networks (CNNs), have achieved excellent performance in high-voltage circuit breaker (HVCB) fault diagnosis. However, CNN ...relies heavily on massive data. When the amount of data decreases, the fault diagnosis performance drops severely. To settle these problems, a few-shot transfer learning (FSTL) with attention mechanism (AM) to realize the mechanical fault diagnosis of HVCBs is proposed. First, a one-dimensional CNN with AM is used to extract the fault features of HVCBs. The introduction of the AM makes CNN pay more attention to the interesting part of the fault signal to extract discriminative features. Then, domain adaptive transfer learning is used to realize a reliable diagnosis of HVCBs in small samples.The subdomain adaptation is adopted to adjust the distribution of related subdomains under the same category. The proposed subdomain adaptation can not only align the global distribution well but also effectively align the distribution of the same category of subdomains. Experimental results show that the FSTL proposed can achieve highly accurate and robust fault diagnosis of HVCBs with few-shot on-site. Compared with the traditional methods, the FSTL is obvious and provides a reliable reference for the diagnosis of HVCBs.
This work proposes a fully convolutional neural network (CNN) for real-time speech enhancement in the time domain. The proposed CNN is an encoder-decoder based architecture with an additional ...temporal convolutional module (TCM) inserted between the encoder and the decoder. We call this architecture a Temporal Convolutional Neural Network (TCNN). The encoder in the TCNN creates a low dimensional representation of a noisy input frame. The TCM uses causal and dilated convolutional layers to utilize the encoder output of the current and previous frames. The decoder uses the TCM output to reconstruct the enhanced frame. The proposed model is trained in a speaker- and noise-independent way. Experimental results demonstrate that the proposed model gives consistently better enhancement results than a state-of-the-art real-time convolutional recurrent model. Moreover, since the model is fully convolutional, it has much fewer trainable parameters than earlier models.
In recent years, neural network-based methods have shown promising results in hyperspectral image (HSI) denoising area. Real HSIs exhibit substantial variations in noise distribution due to various ...factors such as different imaging techniques, camera variations, imaging environments, and hardware aging. In this paper, we develop an eigenimage plus eigennoise level map guided convolutional neural network for HSI denoising. Our main idea is to perform eigendecomposition on HSIs, utilize the low-rank property of HSIs in the spectral dimension and approximate the spectral vectors in a low-dimensional orthogonal subspace, where representation coefficients are called eigenimages. Besides eigenimages, we make use of estimated eigennoise level map as an input to guide the network for denoising. The proposed network can be constructed without restriction in the number of eigencomponents by using all eigenimages and eigennoise level maps of training noisy-clean pairs. In the inference part, the trained network can be used to remove noise in observed eigenimages without restriction in the number of eigencomponents, and an underlying clean image HSI can be estimated by performing orthogonal projection back. Experimental results on both simulated and real HSIs demonstrate the effectiveness of our trained Eigen-CNN compared with state-of-the-art HSI denoising methods. A MATLAB demo of this work is available at https://github.com/LinaZhuang/HSI-denoiser-Eigen-CNN for the sake of reproducibility.
Connected and Automated Vehicles (CAVs), owing to their characteristics such as seamless and real-time transfer of data, are imperative infrastructural advancements to realize the emerging smart ...world. The sensor-generated data are, however, vulnerable to anomalies caused due to faults, errors, and/or cyberattacks, which may cause accidents resulting in fatal casualties. To help in avoiding such situations by timely detecting anomalies, this study proposes an anomaly detection method that incorporates a combination of a multi-stage attention mechanism with a Long Short-Term Memory (LSTM)-based Convolutional Neural Network (CNN), namely, MSALSTM-CNN. The data streams, in the proposed method, are converted into vectors and then processed for anomaly detection. We also designed a method, namely, weight-adjusted fine-tuned ensemble: WAVED, which works on the principle of average predicted probability of multiple classifiers to detect anomalies in CAVs and benchmark the performance of the MSALSTM-CNN method. The MSALSTM-CNN method effectively enhances the anomaly detection rate in both low and high magnitude cases of anomalous instances in the dataset with the gain of up to 2.54% in F-score for detecting different single anomaly types. The method achieves the gain of up to 3.24% in F-score in the case of detecting mixed anomaly types. The experiment results show that the MSALSTM-CNN method achieves promising performance gain for both single and mixed multi-source anomaly types as compared to the state-of-the-art and benchmark methods.
The rapidly developed Health 2.0 technology has provided people with more opportunities to conduct online medical consultation than ever before. Understanding contexts within different online medical ...communications and activities becomes a significant issue to facilitate patients' medical decision making process. As a subcategory of machine learning, neural networks have drawn increasing attentions in natural language processing applications. In this article, we focus on modeling and analyzing the patient-physician-generated data based on an integrated CNN-RNN framework, in order to deal with the situation that patients' online inquiries are usually not very long. A so-called DP-CRNN algorithm is developed with a newly designed neural network structure, to extract and highlight the combination of semantic and sequential features in terms of patient's inquiries. An intelligent recommendation method is then proposed to provide patients with automatic clinic guidance and pre-diagnosis suggestions, in which a clustering mechanism is utilized to refine the learning process with more precise diagnosis scope and more representative features. Experiments based on the collected real world data demonstrate the effectiveness of our proposed model and method for intelligent pre-diagnosis service in online medical environments.
The identification of water bodies from aerial images using semantic segmentation networks can provide accurate information for ecological monitoring, flood prevention, and disaster reduction. ...Outliers on aerial images might reduce interclass separability and thus cause discontinuous prediction of water bodies. The fusion of global context information is helpful to solve this problem. However, the existing global prior representation does not provide sufficient information for identifying a large number of multi-scale objects and outliers. In this study, a dense pyramid pooling module (DensePPM) was introduced to extract global prior knowledge with a dense scale distribution. The ablation experiments showed that the models using the DensePPM had higher values of IoU, F1-score, and Recall than that using pyramid pooling module (PPM), showing that the proposed module could capture more global context information of outliers under multi-scale scenarios. A robust deep learning network named DensePPMUNet-a based on the DensePPM was then proposed for segmenting water bodies from aerial images. The comparative experiments with different datasets demonstrated that the DensePPMUNet-a outperformed U-Net, CE-Net, MultiResUNet, ResUNet-a, PSPNet, LANet, DeepLabV3, MANet, and FactSeg.
