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- MakaleYayıncıda açık erişim2024Scopus SJRQ3Fastener and rail surface defects detection with deep learning techniques
Yilmazer, Merve; Karakose, Mehmet
The railways, which are frequently used by countries for both passenger and freight transportation, should be checked periodically. Controls made with classical methods are slow and there are often overlooked faults. This work suggests a novel deep learning-based technique for identifying fastener and railway track surface defects. Within the scope of the proposed method, firstly, the railroad track was observed using an autonomous drone. Shaky images in the recorded video were removed with a video stabilization algorithm. Frames were created and labeled from the video, and rail and fastener regions were detected using the Faster R-CNN deep neural network. Fault detection was performed through ResNet101v2-based classification using different datasets for identifying surface detects in rails and different datasets for the detection of fasteners. The proposed method was experimentally shown to have a 98% accuracy rate for detecting rail surface flaws and a 95% accuracy rate for detecting fastener flaws. A user interface was developed to display the identified faulty images on computers, tablets, and mobile phones via a mobile application. The system, which was previously proposed in a different study, was retrained by going through the video stabilization step, thus improving the fault detection rate, and the method was also included in the user interface module. This study contributes to the processing of ever-increasing video data with deep learning-based methods. It is also anticipated that it will benefit researchers working in the field of railway non-contact fault detection.
AtıfScopus6OpenAlex6
İncele - BildiriAçık Erişim2023Yolov5 Based Fault Detection Approach in Railway Components
Yilmazer, Merve; Karakose, Mehmet
For safe transportation on the railway, the faults occurring in its components must be detected and repaired. Recently, fault detection methods have been developed on railway visual data using methods such as image processing, machine learning and deep learning. Especially since deep learning-based algorithms have a self-learning structure, the data used in model training and testing significantly affect the performance of the model. In the proposed method within the scope of this study, railway visual data were obtained using autonomous drone. YOLOv5 deep neural network is trained to detect faults that may occur in various components such as rail, fastener, sleeper and ballast in the railway. In order to increase the efficiency of the model, images obtained under different lighting conditions were included in the data set. In addition, by using the OpenCV library, the video stabilization process and the vibrations that occur during data acquisition in the video are eliminated. Thus, with the proposed method, it has been shown that the errors due to many components in the railway can be detected with a mAP rate of 94.5%.
AtıfScopus5OpenAlex3İncele - BildiriAçık Erişim2022Railway Condition Monitoring and Fault Detection Based on YOLOv4
Yilmazer, Merve; Karakose, Mehmet
Regular maintenance of the line is critical for safety in railway transportation, which constitutes an important part of the transportation system. Due to the high error rate of manual fault detection methods, non-contact fault detection methods have been developed. Railway switch state and level crossing faults are frequently encountered in the occurrence of train accidents. A new method based on YOLOv4 has been proposed for condition monitoring and fault detection in these rail sections. The YOLOv4 deep neural network was trained using four class label datasets consisting of real railway visual data. Model evaluated using test data. Performance evaluation was made using evaluation metrics. Experimental results showed that the model could detect correctly with 96.8% accuracy.
AtıfScopus8OpenAlex9İncele