Classification of rail defect based on B-type display image using deep learning method

The rail defect detection is the main method to ensure that the railway transportation is safe. The availability of rail defect information enables the railway departments to determine the integrity of the steel rail and provide suitable plans for railway operation and maintenance. However, the current rail defect detection still relies on the traditional method which require high manpower intensity and time consuming. The high manpower at the current state is unable to cater for the growing need of the railway industries. Furthermore, the traditional method is prone to errors and mistake which reduces the accuracy of the defect detection process. Therefore, this study aims to propose an automated recognition method based on machine learning and image processing to provide more efficient defect detection process while reducing the need of manpower. To achieve that aim, the objectives are to: (1) To classify the steel rail defect by using manpower; (2) To develop deep learning models to classify steel rail defect based on B-type display image; and (3) To optimize deep learning models with different variations of epoch. In phase 1, a total of 6000 rail defect images has been collected from China Railway Hohhot Railway Department. The defects were classified and identified. In phase 2, a newly developed model ResNet50 has been developed for steel rail defect identification and classification. This study uses 5000 steel rail defect images as training data to train ResNet50 model, and then using 1000 steel rail images as testing data to validate model structure. In phase 3, the newly developed ResNet50 are optimized by varying the parameter values of the model framework, 14 final data analysis results were finally obtained. The analysis of the fit and convergence of data results shows that the ResNet50 model can obtain optimal results at Epoch11. This study found that the overall accuracy of the proposed ResNet50 model was 100% in the test dataset and the detection time of a single defect image was 156 ms/ image, while the remaining three deep learning GoogleNet, VGGNet and AlexNet methods were <95%. The comparative results show that the proposed ResNet50 model has the potential to be applied to the automatic identification and classification of large-scale rail defects

Development of an FPGA system for parallel processing of railway non-destructive testing data

Cracks in rails are bad news; they cause accidents and cost money due to delays, as well as incurring repair costs. Inspection of tracks is required in order to find small cracks before they become dangerous. Early detection could also allow repair work which needs maintenance possession on railways to be planned. Non-destructive testing (NDT) is commonly used in rail crack inspection. Alternating Current Field Measurement (ACFM) is one of the latest NDT techniques to be used in crack measurement. This technique is able to detect surface breaking cracks in metals and measure them with proper processing of the non-destructive testing data. In the first part of this dissertation, the current limitations of inspection using ACFM techniques will be laid out. The content that follows describes a high-speed data processing chain for non-destructive testing data, as implemented using an FPGA development board. Multiple ACFM probes are used in practice to cover the surface of the track. Meanwhile, the data collected are parallel processed within the FPGA device. Here, the latest progress and the achievements of this project will be shown using proposed structure diagrams and initial results

Railway defect detection method: A review

The railway is indeed one of the main transportations means in the world. However, with the rapid development and advancement of the railway industries, more railways accidents occur mainly due to its defects which result in economic losses. Traditionally, the railway defect detections process which is deems to be dirty, difficult and dangerous are done manually by the railway maintenance workers. In the recent years, many sophisticated equipment such as portable detectors, track inspection trolleys, track comprehensive inspection vehicles, etc had been developed. This article outlines two main mode of inspection namely static and dynamic inspection, which are commonly used in the railway defect detection and maintenance work. Furthermore, the railway inspection equipment used by the major countries are summarized and the impact on railway inspection based on deep learning and artificial intelligence are appropriately predicted

Autonomous Pedestrian Detection in Transit Buses

This project created a proof of concept for an automated pedestrian detection and avoidance system designed for transit buses. The system detects objects up to 12 meters away, calculates the distance from the system using a solid-state LIDAR, and determines if that object is human by passive infrared. This triggers a visual and sound warning. A Xilinx Zynq-SoC utilizing programmable logic and an ARM-based processing system drive data fusion, and an external power unit makes it configurable for transit-buses

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Advances in Sensors and Sensing for Technical Condition Assessment and NDT

The adequate assessment of key apparatus conditions is a hot topic in all branches of industry. Various online and offline diagnostic methods are widely applied to provide early detections of any abnormality in exploitation. Furthermore, different sensors may also be applied to capture selected physical quantities that may be used to indicate the type of potential fault. The essential steps of the signal analysis regarding the technical condition assessment process may be listed as: signal measurement (using relevant sensors), processing, modelling, and classification. In the Special Issue entitled “Advances in Sensors and Sensing for Technical Condition Assessment and NDT”, we present the latest research in various areas of technology