# Remote Sensing and Machine Learning for Safer Railways: A Review
## 3 Descriptive Analysis
### Methods Reported
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Model Training and Measures**} & \multicolumn{2}{c}{**Image Processing**} \\ \hline
**Acronym** & **Meaning** & **Acronym** & **Meaning** \\ \hline Conv. & Convolution & RES & Resolution \\ WD & Weight decay & fps & Frames per Second \\ LR & Learning rate & bbox & Bounding Box \\ mAP & Mean Average Precision & ROI & Region of Interest \\ MAE & Mean Absolute Error & BCE & Binary Cross-Entropy \\ IoU & Intersection over Union & SIFT & Scale-Invariant Feature Transform \\ rIoU & Relative Intersection over Union & SVM & Support Vector Machine \\ mIoU & Mean Intersection over Union & RSDD & Remote Sensing Data Deconvolution \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Acronyms used in model development and image processing. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Title** & **Method(s)** & **Architecture** & **Images** & **Metrics** \\ \hline Densely pyramidal & & 2 pooling layers. & & Structural similarity index \\ residual network for & & Pyramid pooling: 1/2 and & Images: 13,000 & (SSM): \\ UAV-based railway & & Optimization method: & RES: 512 \(\times\) 512 & Test I: 93.36\% \\ images dehazing [59] & & Adam. & Test II: 91.06\% \\ \hline Fault Detection from & & 24 layers, including 5 & \\ Images of Railway Lines & & convolutional and 5 fully & \\ Using the Deep Learning & \multirow{2}{*}{New CNN (ESA)} & connected pooling, & Images: 383 \\ Model Built with the & & Input: 300 \(\times\) 300 pixels; & RES: not fixed \\ TensorFlow Library [22] & & Activation: SoftMax. & \\ \hline Fully Decoupled Residual & & 19 layers: 14 encoder, 5 & \\ ConvNet for Real-Time & Deep Fully Decoupled & \multirow{2}{*}{Images: 3000} & rIoU: 80.99 \\ Railway Scene Parsing of & & Residual Convolutional & \\ UAV Aerial Images [28] & & Inputs: 256 \(\times\) 256, 128 \(\times\) 128, 64 \(\times\) 64, 128 \(\times\) 128, 256 \(\times\) 256, 512 \(\times\) 512; & \\  & & Initial LR: 5e\({}^{-4}\). & \\ \hline High-Speed Railway & \multirow{2}{*}{Improved conditional} & \multirow{2}{*}{Generator: 12 layers;} & \multirow{2}{*}{Images: 2529} \\ Intruding Object Image & & deep convolutional & \\ Generating with & generative adversarial & & \\ Generative Adversarial & network (C-DCGAN) & & \\ Networks [19] & & & \\ \hline Hybrid Deep Learning & & 3 convolutional layers: U1, & \\ Architecture for Rail & & U2, and U3, followed by & \\ Surface Segmentation and & & three ReLU layers. & \\ Surface Defect & & Kernel size: 3; & RES: 3840 \(\times\) 2160 & SSIM F-measure: 0.967 \\ Detection [62] & & Iterations: 20 epochs; & RES: 1920 \(\times\) 1080, 30 fps & MAE: 0.013. \\  & & LR: Initial 5 \(\times\) 10\({}^{-5}\), decay & \\  & & 0.1 each iteration. & \\ \hline Multi-Target Defect & & 21 layers. & \\ Identification for Railway & & & \\ Track Line Based on Image & \multirow{2}{*}{YOLOv3; TLMDDNet} & \multirow{2}{*}{ \begin{tabular}{} \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{} \end{tabular}  } \\ Processing and Improved & & & & \\ YOLOv3 Model [42] & & & \\ \hline \multirow{6}{*}{Obstacle Detection of Rail } & \multirow{6}{*}{Improved-YOLOv4} & 3 pooling layers. & \multirow{6}{*}{ \begin{tabular}{} \end{tabular}  } \\ Transit Based on Deep & & & \\ Learning [39] & & Improved-YOLOv4: more & & \\ Learning [39] & & Stage 1 LR: 0.001, & \\  & & minimum LR: 0.0001, & \\  & & Stage 2 LR: 0.0005 and & & \\  & & 0.00005. & \\ \hline \multirow{6}{*}{Rail Fastener Detection of } & \multirow{6}{*}