An optimized railway fastener detection method based on modified Faster R-CNN

Bai, Tangbo; Yang, Jianwei; Xu, Guiyang; Yao, Dechen

doi:10.1016/j.measurement.2021.109742

Cited by 68 publications

(27 citation statements)

References 30 publications

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“…To demonstrate the effectiveness of the proposed method, we have also compared the relevant methods in the literature. These methods consist of traditional computer vision techniques [2,11] and deep learning-based techniques [9,10,19]. Fastener defect classification results with different methods are given in Table 5.…”

Section: Resultsmentioning

confidence: 99%

“…The proposed hybrid approach focuses on the detection of different fasteners under different lighting and scaling conditions, rather than identifying defects [10]. Bai et al [11] proposed a faster detection and defect classification method with a modified Faster RCNN (region based convolutional neural networks) and a support vector data identification-based method. Defective and robust fasteners were taken into consideration during the detection and classification stages.…”

Section: Introductionmentioning

confidence: 99%

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Defect Classification of Railway Fasteners Using Image Preprocessing and a Lightweight Convolutional Neural Network

2021

Turk J Elec Eng & Comp Sci

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Railway fasteners are used to securely fix rails to sleeper blocks. Partial wear or complete loss of these components can lead to serious accidents and cause train derailments. To ensure the safety of railway transportation, computer vision and pattern recognition-based methods are increasingly used to inspect railway infrastructure. In particular, it has become an important task to detect defects in railway tracks. This is challenging since rail track images are acquired using a measuring train in varying environmental conditions, at different times of day and in poor lighting conditions, and the resulting images often have low contrast. In this study, a new method is proposed for the classification of defects on rail track fasteners. The proposed approach uses image enhancement to first filter the rail images and obtain a high contrast image. Then, the rail track and sleeper positions are determined from the high contrast image. The location of the fastener is determined by applying the Line Local Binary Pattern method and the defects of the fastener are classified using an improved lightweight convolutional neural network (LCNN) model. Features are extracted from two fully connected layers of the developed LCNN model and the feature vector is constructed by concatenating these layers. The concatenated features are processed using a number of machine learning methods and the optimum classifier is chosen. Experimental results show that Cubic SVM gives the best results with a detection accuracy rate of 99.7%.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Defect Classification of Railway Fasteners Using Image Preprocessing and a Lightweight Convolutional Neural Network

2021

Turk J Elec Eng & Comp Sci

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“…RPN is a significant improvement over the prior Faster R-CNN algorithms. Instead of selective searching, the RPN produces candidate areas using a sliding window technique [ 36 ]. The convolution feature layer receives a 256-dimensional eigenvector through the sliding window.…”

Section: Proposed Approachmentioning

confidence: 99%

Multithreshold Image Segmentation Technique Using Remora Optimization Algorithm for Diabetic Retinopathy Detection from Fundus Images

Vinayaki

Kalaiselvi

2022

Neural Process Lett

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One of the most common complications of diabetes mellitus is diabetic retinopathy (DR), which produces lesions on the retina. A novel framework for DR detection and classification was proposed in this study. The proposed work includes four stages: pre-processing, segmentation, feature extraction, and classification. Initially, the image pre-processing is performed and after that, the Multi threshold-based Remora Optimization (MTRO) algorithm performs the vessel segmentation. The feature extraction and classification process are done by using a Region-based Convolution Neural Network (R-CNN) with Wild Geese Algorithm (WGA). Finally, the proposed R-CNN with WGA effectively classifies the different stages of DR including Non-DR, Proliferative DR, Severe, Moderate DR, Mild DR. The experimental images were collected from the DRIVE database, and the proposed framework exhibited superior DR detection performance. Compared to other existing methods like fully convolutional deep neural network (FCDNN), genetic-search feature selection (GSFS), Convolutional Neural Networks (CNN), and deep learning (DL) techniques, the proposed R-CNN with WGA provided 95.42% accuracy, 93.10% specificity, 93.20% sensitivity, and 98.28% F-score results.

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“…Prior to the cancerous cell classification, the layers of the convolution are employed to derive the feature maps from the augmented images. RCNN 25 is the most widely used deep learning approach utilized to predict the target in the image processing techniques due to its dominant features. However, CNN requires 2000 region proposals to train the RCNN algorithm, which might have led to low prediction results with the utilization of more amount of resources.…”

Section: Proposed Deep Learning‐based Approach To Detect the Lung Cancermentioning

confidence: 99%

Improving lung cancer detection using faster region‐based convolutional neural network aided with fuzzy butterfly optimization algorithm

Sinthia

Malathi²,

Anitha

et al. 2022

Concurrency and Computation

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Lung cancer is the most deadly type of cancer and it is caused by genetic variations in lung tissues. Other causes of lung cancer are alcohol, smoking, and threatening gas exposures. The diagnosis of lung cancer is an intricate task and early detection of lung cancer can help to get the exact treatment in advance. The application of a computer-aided diagnosis process helps to predict lung cancer earlier, nonetheless, it does not provide better accuracy. The overfitting nature of features and dimensionality of lung cancer can prevent it from obtaining maximum accuracy. Hence, we proposed a novel faster region convolutional neural network (RCNN) based fuzzy butterfly optimization algorithm (FBOA) to achieve better prediction accuracy and effectiveness. The proposed Faster RCNN can provide better positioning of lung cancer swiftly and effectively and the FBOA approach can be used to perform two-stage classification. The fuzzy rules used in the FBOA can be utilized to find the severity of the lung cancer and differentiate the Benign stage and Malignant stage effectively.The experimental analyses are performed in MATLAB simulation. The preprocessing of images is performed by different tools from MATLAB and is to format the images as required. The cancer imaging archive (TCIA) dataset is utilized to analyze the performance of the proposed method and compared with various state-of-art works. The performance of the proposed method is evaluated by using different evaluation metrics such as precision, recall, F-measure, and accuracy and attained 99, 98, 99, and 97% respectively. Thus, our proposed method outperforms all the other approaches.

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An optimized railway fastener detection method based on modified Faster R-CNN

Cited by 68 publications

References 30 publications

Defect Classification of Railway Fasteners Using Image Preprocessing and a Lightweight Convolutional Neural Network

Defect Classification of Railway Fasteners Using Image Preprocessing and a Lightweight Convolutional Neural Network

Multithreshold Image Segmentation Technique Using Remora Optimization Algorithm for Diabetic Retinopathy Detection from Fundus Images

Improving lung cancer detection using faster region‐based convolutional neural network aided with fuzzy butterfly optimization algorithm

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