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Objectives. This study is aimed at determining whether CT-based radiomics models can help differentiate renal angiomyolipomas with minimal fat (AMLmf) from other solid renal tumors. Methods. This retrospective study included 58 patients with a postoperative pathologically confirmed AMLmf (observation group) and 140 patients with other common renal tumors (control group). Non-contrast-enhanced CT and contrast-enhanced CT data were evaluated. Radiomics features were extracted from manually delineated volume of interest (VOIs). The least absolute shrinkage and selection operator (LASSO) regression was used for feature screening. Five classifiers, including logistic regression, multilayer perceptron (MLP), support vector machine (SVM), k -nearest neighbor (KNN), and logistic regression (LR), were used, with leave-out validation (128 training, 60 testing). The diagnostic performance of the classifier was evaluated and compared by receiver operating characteristic curve (ROC) analysis. Results. Among the 1029 extracted features, prediction models of AMLmf were composed, by 2, 10, 4, and 9 selected features for precontrast phase (PCP), corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP), respectively. Models of CMP and NP achieved adequate performance after using MLP classifier, with prediction accuracy of 0.767 (AUC 0.85, sensitivity 0.76, and specificity 0.78) and 0.783 (AUC 0.83, sensitivity 0.79, and specificity 0.78), respectively. MLP model of features selected from the combination of the all features had the best diagnostic performance (accuracy 0.8500, sensitivity 0.8095, specificity 0.9444, and AUC 0.9193). Conclusions. Radiomics features may help to distinguish benign AMLmf from common malignant kidney masses, which may contribute to the selection of interventions for renal tumors. [ABSTRACT FROM AUTHOR]

The work state of catenary support components (CSCs) is essential for trains’ powering safety under high-speed operation. However, because of the difficulty of positioning the CSCs whose sizes are much different, some detection methods based on classical deep learning technologies only position one specific component, and the others that can position all categories CSCs have poor performance. A new position method for 12 categories of CSCs called CSC structure inference net (CSCSIN) is proposed in this article. This method is composed of three sections, including the feature extraction with a multilevel feature, feature inference with structure prior information, regression, and classification. In the feature extraction section, to extract small-scale CSCs’ features, a modified VGG16, which is used to generate a legible feature map based on the fusion of low and high layers’ features, is proposed, and multiscales anchors for RoIs prediction are applied to suit multiscale CSCs. In the feature inference section, considering application scenarios and fixed position relationship between CSCs, the scene visual feature and position information between targets are used to iteratively update each target state in structure inference net, and the final state of each target is used for its classification in regression and classification section. Based on the modification in the feature extraction section and the combination of relevant information of CSCs, CSCSIN is efficient and sensitive to all 12 CSCs whose scales are much different. The experimental results demonstrate that the position precision of CSCSIN is superior to other classical models and relevant methods. [ABSTRACT FROM AUTHOR]

Brace sleeve (BS) fasteners, i.e., nut and bolt, are small components but play essential roles in fixing BS and cantilever in railway catenary system. They are commonly inspected by onboard cameras using computer vision to ensure the safety of railway operation. However, most BS fasteners cannot be directly localized because they are too small in the inspection images. Instead, the BS is first localized for detecting the BS fastener. This leads to a new problem that the localized BS boxes may not contain the complete BS fasteners due to low localization accuracy, making it infeasible to further diagnose the fastener conditions. To tackle this problem, this article proposes a novel pipeline for BS fastener looseness diagnosis. First, the competitive deep learning model Faster RCNN ResNet101 is used to coarsely localize BSs. Second, an action-driven reinforcement learning agent is adopted to refine the coarse-localized boxes through a dynamic position searching process. Then, BS fasteners are extracted from the refined localized BS image by the deep segmentation model YOLACT++, which is fast and interpretable. Finally, a looseness diagnosis criterion based on segmented information are proposed. We evaluate the performance of submodels independently and the overall performance of the whole model on a real-life catenary image dataset collected from a high-speed line in China. The test results show that the proposed method is effective for BS looseness detection in railway catenary. [ABSTRACT FROM AUTHOR]

This article proposes an automatic high-precision detection method for structure parameters of catenary cantilever devices (SPCCDs) using 3-D point cloud data. The steps of the proposed detection method are: 1) segmenting and recognizing the components of the catenary cantilever devices, 2) extracting the detection plane and backbone component axis of catenary cantilever devices, and 3) detecting the SPCCD. The effective segmentation of components is critical for structure parameter detection. A point cloud segmentation and recognition method based on three-dimensional convolutional neural networks (3-D CNNs) is introduced to determine the different components of the catenary cantilever devices. Compared with traditional unsupervised clustering procedures for point cloud segmentation, the proposed method can improve the segmentation accuracy, does not require complex tuning procedures of parameters, and improves robustness and stability. Additionally, the segmentation method defines a recognition function, facilitating the analysis of the structural relationship between objects. Furthermore, we proposed an improved projection random sample consensus (RANSAC) method, which can effectively divide the detection plane of catenary cantilever devices to solve the multicantilever device occlusion problem. With RANSAC, it is also possible to precisely extract the backbone component axis and enhance parameter detection accuracy. The experimental results show that the structure angle and steady arm slope’s error accuracy can achieve 0.1029° and 1.19%, respectively, which indicates the proposed approach can precisely detect the SPCCD. [ABSTRACT FROM AUTHOR]

This article aims to present a defect detection approach for catenary rod-insulators based on unsupervised learning. The idea of the proposed detection method is that 1) the regions of catenary insulator pieces are separated first; 2) then, the real samples of the segmented catenary insulator pieces are reconstructed as the foreground, and through the difference between the segmented and reconstructed images, the defect regions are separated as the background; and 3) the defect levels are evaluated according to the separated defect regions. The separation effectiveness of the foreground and background in the second stage is the most vital key. Therefore, a reconstruction and classification convolutional autoencoder network (RCCAEN) is built. Compared with standard autoencoders (AEs), the proposed convolutional AE network can improve the system real time and the ambiguity of the edges of the recovered images. Moreover, it can avoid the interference from the incorrectly segmented components by incorporating a small classification network after the latent support vector. Besides, the dropout layer is added behind the input images to enhance the robustness of the proposed network to noise. In this article, first, the regions of catenary insulator pieces are segmented by combining Mask regions with CNN features (R-CNN) and curving fitting method. And then, the defect regions are extracted by the proposed network RCCAEN. Finally, the defect levels are judged by the indexes of the density-based spatial clustering of applications with noise. The experimental results show that the proposed approach can accurately detect the defects of catenary insulator severe pollution. [ABSTRACT FROM AUTHOR]

Brace sleeve screws are an important component of catenary support devices in high-speed railways. According to the statistical data, there is a much risk for failure of brace sleeve screws during the operation of the catenary system. In order to obtain better results on the fault diagnosis for brace sleeve screws, it is of vital importance to develop a high-precision detection approach for brace sleeve screws. With the aim of solving the problems that the size of the brace sleeve screw is too small and the employed previous methods are difficult to detect accurately, a novel object detection network based on an improved Faster RCNN is proposed. Through the analysis of the factors that influence the detection accuracy for brace sleeve screws and the characteristics of original Faster RCNN, the concepts of discrimination maps and proposal maps are introduced for the improved Faster RCNN. In order to retain the characteristic information of brace sleeve screws, the high-resolution discrimination maps are adopted to identify the object type and the low-resolution proposal maps are adopted to generate the candidate boxes of region with an appropriate anchor scale configuration. Experimental results show that the proposed method has a higher detection accuracy for brace sleeve screws. In addition, compared with original Faster RCNN implementation based on the VGG-16 and ResNet-101 backbone, the detection accuracy of the improved Faster RCNN is increased by 8%. It also provides a new idea for general object detection of small targets. [ABSTRACT FROM AUTHOR]

The goal of this article is to develop a universal anomaly detection approach for catenary support components (CSCs) based on the generative adversarial networks (GANs). As the long-term operation of railway system, a wide range of failures, which threaten the safe operation of vehicles, perhaps happen to CSCs. Until now, it is hard to design a generic detection system to recognize all these kinds of failures because each defect needs a special detecting algorithm for different fault signatures. The lack of anomaly samples also makes it difficult for supervised learning methods to detect effectively. In this article, a novel approach, which combines deep convolution neural networks (DCNNs) with GANs, is proposed to estimate whether failures happen and give an alarm to stop the accident. First, an object location model is trained by DCNNs to obtain numerous samples of CSCs. Second, a generative model based on deep convolutional GAN (DCGAN) is constructed to find a good mapping from image space to high-dimensional feature spaces implicitly. Finally, an anomaly rating criterion is used to diagnose images. Two typical components of CSCs, the insulator that has big body and the isoelectric line that has tiny characters, are tested here. Experiments show that the proposed method can correctly judge anomalous images of CSCs and possess a good generic failure detection ability in this single framework. [ABSTRACT FROM AUTHOR]

The catenary support components (CSCs) are the most important devices in high-speed railways to support contact lines for powering trains. To estimate the states of CSCs, it is very necessary to locate their positions in a monitoring system based on computer vision. Considering the application scenarios and characteristics of CSCs, an automatic and quick positioning system is designed in this paper to simultaneously position the multiscale CSCs with 12 categories. In the system, an effective framework called CSCs network (CSCNET) is presented, which cascades the coarse positioning network and the fine positioning network to reduce multiscale differences between different CSCs. In the coarse positioning network, a new unsupervised clustering algorithm based on the relative positioning information is proposed to classify the catenary images. Then, a convolutional neural network (CNN) classification network is trained to extract the structural features of catenary images and generate the proposal regions with labels. In the fine positioning network, a modified CNN positioning framework is applied to obtain the accurate positions of CSCs based on the coarse positioning results. Due to the special lightweight structure with a classification network, the relative position information is applied and makes the CSCNET sensitive to small-scale components. The experimental results from some high-speed railway lines in China show that the proposed system has obvious advantages in the CSCs positioning. The mean average precision and frames per second of CSCNET reach 0.837 and 2.17, respectively. Compared with some popular convolutional networks [faster region-based CNN (Faster R-CNN), etc.] and a typical positioning method, the proposed system significantly improves the AP without increasing the computational time. [ABSTRACT FROM AUTHOR]

Split pins (SPs) play an important role in fixing joint components on catenary support devices (CSDs) of high-speed railway. The occurrence of loose and missing defects of SPs could make the structure of CSDs unstable. In this paper, we present a three-stage automatic defect inspection system for SPs mainly based on an improved deep convolutional neural network (CNN), which is called PVANET++. First, SPs are localized by PVANET++ and the Hough transform & Chan–Vese model, and then, three proposed criteria are applied to detect defects of SPs. In PVANET++, a new anchor mechanism is applied to produce suitable candidate boxes for objects, and multiple hidden layer features are combined to construct discriminative hyperfeatures. The performance of PVANET++ and several recent state-of-the-art deep CNNs is compared in a data set that is collected from a 60-km rail line. The results show that our model is superior to others in accuracy, and has a considerable speed. [ABSTRACT FROM AUTHOR]

The isoelectric line is an important component that connects the steady arm and the drop bracket of catenary in high-speed railway. The loose strands of isoelectric line can be commonly observed in real-life applications. In this paper, an automatic fault detection system for the loose strands of the isoelectric line is proposed. This system consists of three stages. First, a convolutional neural network is adopted to extract the isoelectric line features. To accurately and quickly learn these features, an improved feature extraction network, called as the isoelectric line network, is presented. Using the images captured from catenary inspection vehicles, the image areas that contain the isoelectric lines are obtained based on the Faster region-based convolutional neural network. Second, the image segmentation is carried out based on the Markov random field model. And, the accurate isoelectric line pixels are obtained from the smallest image area extracted from the first stage. In the final stage, the fault state is given by analyzing the quantity of the independent connection regions and the pixels’ standard deviation. Experimental results show that the proposed system has a high detection accuracy. Furthermore, compared with the convolutional neural networks (the Simonyan and Zisserman model and the Zeiler and Fergus model) and a typical detection method (Histogram of Oriented Gradient + Support Vector Machine), the proposed network has better performance for the isoelectric line location. [ABSTRACT FROM PUBLISHER]

The excitation and vibration triggered by the long-term operation of railway vehicles inevitably result in defective states of catenary support devices. With the massive construction of high-speed electrified railways, automatic defect detection of diverse and plentiful fasteners on the catenary support device is of great significance for operation safety and cost reduction. Nowadays, the catenary support devices are periodically captured by the cameras mounted on the inspection vehicles during the night, but the inspection still mostly relies on human visual interpretation. To reduce the human involvement, this paper proposes a novel vision-based method that applies the deep convolutional neural networks (DCNNs) in the defect detection of the fasteners. Our system cascades three DCNN-based detection stages in a coarse-to-fine manner, including two detectors to sequentially localize the cantilever joints and their fasteners and a classifier to diagnose the fasteners’ defects. Extensive experiments and comparisons of the defect detection of catenary support devices along the Wuhan–Guangzhou high-speed railway line indicate that the system can achieve a high detection rate with good adaptation and robustness in complex environments. [ABSTRACT FROM AUTHOR]