# Remote Sensing and Machine Learning for Safer Railways: A Review
## 3 Descriptive Analysis
### Methods Reported
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Research Method** & **ANN** & **Relevant Publication(s)** \\ \hline \multirow{4}{*}{Convolutional Networks} & \multirow{4}{*}{Y} & [PERSON] et al. (2021) [18], [PERSON] et al. (2019) [19], [PERSON] et al. (2022) [20], \\  & & [PERSON] et al. (2021) [21], [PERSON] et al. (2017) [12], [PERSON] et al. (2022) [22], \\  & & [PERSON] et al. (2017) [23], [PERSON] et al. (2017) [24], [PERSON] et al. (2021) [25], \\  & & [PERSON] et al. (2022) [26], [PERSON] et al. (2022) [27], [PERSON] et al. (2022) [28] \\ \hline \multirow{4}{*}{Non-ANN Methods} & \multirow{4}{*}{N} & [PERSON] et al. (2018) [13], [PERSON] et al. (2016) [29], [PERSON] et al. (2022) [30], \\  & & [PERSON] et al. (2019) [31], [PERSON] et al. (2019) [32], \\  & & [PERSON] et al. (2021) [33], [PERSON] et al. (2019) [34], [PERSON] et al. (2021) [35], \\  & & [PERSON] et al. (2018) [36], [PERSON] et al. (2022) [37], [PERSON] et al. (2010) [35] \\ \hline \multirow{2}{*}{You Only Look Once (YOLO)} & \multirow{2}{*}{Y} & [PERSON] et al. (2021) [39], [PERSON] et al. (2021) [40], [PERSON] et al. (2022) [41], [PERSON] et al. (2020) [42], [PERSON] et al. (2023) [43], [PERSON] et al. (2020) [44] \\ \hline \multirow{2}{*}{Survey/Reviews} & \multirow{2}{*}{N} & [PERSON] et al. (2022) [45], [PERSON] (2021) [46], [PERSON] et al. (2018) [47], [PERSON] et al. (2022) [48] \\ \hline \multirow{2}{*}{Region-Based Convolutional Neural Network (R-CNN)} & \multirow{2}{*}{Y} & [PERSON] et al. (2018) [49], [PERSON] et al. (2022) [50], [PERSON] et al. (2021) [51], [PERSON] et al. (2019) [52] \\ \hline \multirow{2}{*}{Generative Adversarial Networks (GAN)} & \multirow{2}{*}{N} & [PERSON] et al. (2019) [53], [PERSON] et al. (2022) [54] \\ \hline Defect Detection Network (DDNet) & & [PERSON] et al. (2020) [42], [PERSON] et al. (2021) [55] \\ \hline Fully Convolutional Network (FCN) & \multirow{2}{*}{Y} & [PERSON] et al. (2020) [56], [PERSON] et al. (2021) [57] \\ \hline Recurrent Neural Network (RNN) & \multirow{2}{*}{Y} & [PERSON] et al. (2022) [20] \\ \hline Coarse-To-Fine-Extractor (CFE) & \multirow{2}{*}{N} & [PERSON] et al. (2017) [58] \\ \hline Conditional Random Fields (CRF) & \multirow{2}{*}{N} & [PERSON] et al. (2021) [18] \\ \hline Deep Residual Network (ResNet-50) & \multirow{2}{*}{Y} & [PERSON] et al. (2021) [21] \\ \hline Densely Pyramidal Residual Network (DPRnet) & \multirow{2}{*}{Y} & [PERSON] et al. (2020) [59] \\ \hline Global Low-Rank and Non-negative & \multirow{2}{*}{N} & [PERSON] et al. (2021) [60] \\ Reconstruction (GLRNNR) & & \\ \hline Long Short-term Memory (LSTM) & \multirow{2}{*}{Y} & [PERSON] et al. (2022) [20] \\ \hline Neural Network that Directly Consumes Point & \multirow{2}{*}{Y} & [PERSON] et al. (2020) [61] \\ Clouds (PointNet++) & & \\ \hline Rail Boundary Guidance Network (RBGNet) & \multirow{2}{*}{N} & [PERSON] et al. (2022) [62] \\ \hline Self-Organizing Map (SOM) & \multirow{2}{*}{N} & [PERSON] et al. (2013) [14] \\ \hline Wavelet Scattering Networks (WSNs) & \multirow{2}{*}{N} & [PERSON] (2021) [63] \\ \hline Case Studies & \multirow{2}{*}{N} & BNSF (2019) [64] \\ \hline Dropper Fault Recognition Network (DFRN) & \multirow{2}{*}{N} & [PERSON] et al. (2019) [65] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Methods used by each publication. Four publications utilized surveys and reviews. The other publications used a variety of other statistical methods. Table 3 defines the acronyms for the parameters reported in Table 4, which provides some details about the architectures of the ANN model variants, the images analyzed, and the metrics of their effectiveness. Figure 6: Distribution of publication year split by methods.