## 1 Introduction
Visual inspection is limited to detecting only the defects on the rail surface when they are not covered by snow or other obstacles. Dust, dirt and water on the lens can also decrease the detection accuracy. The ultrasonic sensor measures and analyses the waves reflected back [18]. This method is suitable even for detecting the faults inside the rail (railhead and waist) [19]. [PERSON] et al. [20] proposed an ultrasonic testing method with a high-speed phased array. This multi-angle beam approach increases both the speed and the range of detection. [PERSON] and [PERSON] [21] proposed a monitoring method using ultrasonic techniques to measure the crack propagation when forming squats. The results showed that the propagation can be approximated to be linear to the accumulated passing by tonnages up to some degree. The swift advancement of deep learning and other artificial intelligence (AI) technologies has led to significant progress in ultrasonic data processing [22]. This method needs accurate probe configurations and constant precise contact between the probe and the rail [23]. This makes the method suffer from technical challenges due to both environmental and operating conditions. Moreover, this technology faces challenges when detecting near-surface defects [23]. The ECT method is widely applied to inspect conductive material to analyse structural integrity. [PERSON] et al. [24] proposed a new system based on ECT for detecting and locating rail defects and they claimed an accuracy of 98%. The proposed method aimed to analyse eddy current data using wavelet transforms together with a convolutional neural network (CNN). [PERSON] et al. [25] proposed a differential eddy current sensor system on the train for fastener inspection. The proposed model achieved a detection accuracy of 96.79%. [PERSON] et al. [26] developed an advanced eddy current device with 16 channels to analyse the correlation between the depth and phase of a known artificial defect and later applied it to a natural defect specimen. The main challenge of the ECT system is the lift-off effect that affects the ECT signal, causing erroneous data interpretation [27]. Using vibration measurement is another popular method to detect rail defects. Vibrations usually can be measured from the train. [PERSON] et al. [28; 29] proposed a method to improve the axle box acceleration (ABA) measurements for detecting light squats and performing health condition monitoring of insulated joints. One disadvantage of usingABA is that the maximal ABA excited can be greater than 50 g, so a trade-off in sensitivity is needed. [PERSON] et al. [30] proposed using the bogie acceleration (BA) measured for defect detection instead of ABA. A few advantages were emphasised. These two methods are both suitable for inspecting a long distance of rail. One important type of rail defect is squats. [PERSON] et al. [31] stated that almost a third of the recorded failures of the crossing panel were related to squats. Some studies have been performed about squat detection. Squat defects are the type of rail defect focused on in this study. [PERSON] et al. [32] presented a simplified method for squat defect detection using laser scatterometry and the results achieved verified that it is effective in squat detection with both artificial and real defects. [PERSON] et al. [33] utilised 3D reconstruction techniques to improve the performance and the achieved results demonstrated the possibility of using this technique for rail and crossing-nose inspection in railway systems. However, this type of method faces the challenges of the presence of water, dust and dirt on the laser lenses. [PERSON] et al. [34] presented a DCNN approach to analyse images for rail surface defect detection and the experiments showed promising results. The results of DCNN architectures with different sizes and activation functions were tested to analyse the proposed method. However, the camera lens had similar problems as the laser transmitter. [PERSON] et al.