###### Abstract
The components considered for this study include only those that exhibit magnetic properties. Averages of 572.7 h and 670.3 h of delays are incurred in Sweden yearly due to failure of components in track and S&C respectively. To avoid such delays and to ensure safe and reliable operation, tracks and components need to be inspected periodically. Track inspection is a crucial task that has to be carried out periodically to prevent catastrophic accidents and control the condition of the railway infrastructure [7]. Traditionally, rail inspections are carried out by trained inspectors who walk along the track length to search for visible defects and technical deviations. Such manual inspections are labour-intensive, slow and may include human errors, especially in tough winter condition. This method is time-consuming and expensive for railroad companies, especially for long-term and large-scale development projects. Recent technological developments have seen automated inspections systems based on machine vision being utilized for track inspections. Automated rail inspection systems are composed of various functions including, gauge measurement, rail-surface defect detection, rail profile measurement and fastener defect detection [8]. Rail fastening systems are a crucial component in the rail infrastructure as they clamp the rail to the sleepers, maintaining the gauge, preventing the longitudinal and transverse deviation of rails from the sleepers, and preserving the designed geometry of the track. Failures of fasteners reduce the safety of train operations, increase wheel flange wear and may lead to catastrophic accidents [9]. In the past two decades, the application of automated machine vision for fastener inspection has grown significantly; however, the detection method from these rail images have varied over time. In 2007, [PERSON] et al. [10] detected missing hexagonal-headed bolts from rail images using a multilayer perceptron neuron classifier. For hook-shaped fasteners [PERSON] et al. [11] used a neural classifier to detect missing fasteners, employing wavelet transform and principal component analysis to preprocess the railway images. [PERSON] et al. [12] used direction field as a template of the fastener in the rail images and used linear discriminant analysis (LDA) for matching, to obtain the weight coefficient matrix. To model two types of fasteners, [PERSON] et al. [13] used error back propagation algorithm on the rail images and implemented the same on graphical processing unit to achieve real time performance. [PERSON] et al. [14] also introduced a FPGA-based architecture employing the same algorithm on rail images. [PERSON] et al. [15] and [PERSON] [16] used AdaBoost algorithm for detecting fasteners from rail images. [PERSON] et al. [17] adopted image-processing techniques to detect fasteners and its components from the images obtained from visual inspections. [PERSON] et al. [8] adopted structure topic model (STM) to model fasteners and learn from the probabilistic representation of different components within rail images. [PERSON] et al. [18] used line local binary pattern (LLBP) on the rail images to distinguish between normal fasteners and failed fasteners. Support vector machines (SVM) [19], Gabor filters [20] and edge detection [21] methods are other commonly employed techniques to detect fasteners from rail images. Recent advancements in image processing have enabled the use of deep learning to [22, 23] R-CNN [24] detect fasteners from rail images collected during automated visual inspection. The present mode of fastener detection is carried out from images acquired during automated visual inspection of track and its components. They become a complicated task when the rail and its components are concealed due to the presence of rust and dust. Presence of snow, stones and other debris or heavy rain can also cause hindrance in visual inspection and minimise the efficiency in detecting the rail and its components.