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Page 164                            Ji et al. Intell Robot 2021;1(2):151-75  https://dx.doi.org/10.20517/ir.2021.14

               Table 1. Number of papers over time
                Year         Total number of papers                                   Ref.
                2014         1                                                        [60]
                2015         1                                                        [61]
                2016         3                                                        [62-64]
                2017         5                                                        [65-69]
                2018         13                                                       [70-82]
                2019         16                                                       [83-98]
                2020         13                                                       [99-111]
                2021         10                                                       [112-121]


               Table 2. Paper distribution over regions
                Region          Total number of papers            Ref.
                Australia       1                                 [70]
                Canada          1                                 [83]
                China           35                                [65,71-78,84-92,99-107,112-119]
                Germany         1                                 [93]
                Hong Kong       2                                 [94,108]
                India           2                                 [66,67]
                Korea           1                                 [95]
                Netherlands     4                                 [62,63,78,80]
                Russia          2                                 [96,109]
                Singapore       3                                 [81,97,110]
                Swiss           1                                 [60]
                Turkey          3                                 [68,69,111]
                UK              1                                 [120]
                USA             5                                 [61,64,82,98,121]



               Purpose of study: it is observed that detection, classification, and/or localizing rail surface defects including
               various components (rail, insulator, valves, fasteners, switches, track intrusions, etc.) are the most common
               purpose of the studies. There are also papers on predicting maintenance time  and detecting track
                                                                                      [99]
                                [98]
               geometry elevations . Detection and classification tasks are more common than prediction tasks [60,80,87,99,119] .
               Adoption of deep learning models: many deep learning models are adopted by researchers. Table 3
               summarizes the distribution of deep learning models. CNN is the most popular deep learning model being
               adopted; however, many researchers created their own structure or divided their tasks into a few stages.
               CNN has been popular for extracting features and RNN/LSTM has been used for the sequential data type.


               From the summary in Table 3, there are various deep learning methods being adopted in different forms.
               The effectiveness and the results differ from each other depending on the tasks. It is observed that image is
               the most popular input data type used for deep learning applications. However, there is a consistent process
               flow for how to apply the deep learning methods to rail track condition monitoring. First, the image
               acquisition subsystem (cameras/recording devices) is usually installed on rail engineering maintenance
               vehicles to capture raw input data. Second, the raw input data are transferred to the image processing
               subsystem where optional data pre-processing could be performed. Images could be resized, enhanced, have
               noise removed, or cropped for target areas with image processing techniques. Third, the input data are
               prepared for the training and testing of deep learning models. Data are labeled accordingly and then
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