# Remote Sensing and Machine Learning for Safer Railways: A Review
## 4 Discussion  
Table 4 includes an outline of the parameters for each of the ANN methods used, showcasing the variation in the number and type of architectures across different approaches. CNN emerged as the dominant method utilized in the body of publications analyzed. CNNs distinguish themselves through a multi-layer, multi-resolution approach that loosely simulates the visual neural system of the human brain. Research has demonstrated that CNNs perform better than traditional ML techniques in machine vision applications [66]. However, the use of CNN and traditional ML approaches vary based on the specific task [67]. The following subsections expand on the methodological patterns observed, propose a classification of the railway condition monitoring applications reviewed, provide some case studies along with challenges encountered, explore future directions, and discuss the limitations of the study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Title** & **Method(s)** & **Architecture** & **Images** & **Metrics** \\ \hline \multirow{8}{*}{Rail Network Detection from Aerial Imagery using Deep Learning [12]} & \multirow{8}{*}{CNN (ConvNets) and Deconvolutional Neural Networks (DeConvNets)} & 61 layers in 4 different levels: \(16+13+23+9\). & \multirow{8}{*}{Accuracy: 98\%} \\  & & 4 different process levels: & & \\  & & (1) Aerial classifying images; (2) segment rail tracks on aerial images; & & \\  & & (3) coarse-level image segmentation; (4) segment rail tracks at individual pixel level. & & \\  & & LR: 0.96 decay with step size 1000. & & \\ \hline \multirow{8}{*}{Railway Track Fastener Defect Detection Based on Image Processing and Deep Learning Techniques: A Comparative Study [52]} & \multirow{8}{*}{CNN/DCNN/R-CNN} & 14 convolution layers, & \multirow{8}{*}{
\begin{tabular}{} \end{tabular} } \\  & & 3 fully connected. & & \\  & & Kernel size: \(3\times 3\). & & \\  & & Transfer learning: & & \\  & & & Image Processing and & \\  & & VGG-16 & & \\  & & with pre-trained weights & & \\  & & Techniques: A & & \\  & & & from ImageNet. & \\  & & Fine-tuning: use weights & & \\  & & of conv5 and fully & & \\  & & connected layers. & & \\ \hline \multirow{8}{*}{Railway Track Inspection Using Deep Learning Based on Audio to Spectrogram Conversion: An on-the-Fly Approach [20]} & \multirow{8}{*}{2 \(\times\) CNN, RNN, LSTM} & LSTM Activation: hyperbolic tangent, ReLU, and SoftMax. Dropout rate: 0.2. & \multirow{8}{*}{Images: 720} \\  & & Conv1D: kernel: 4: & & \\  & & Activation: hyperbolic & & \\  & & 2 & ReLU, SoftMax; Dropout: 0.1; Filters: & \\  & & & 16-128. & \\  & & Conv2D: kernel: 3 \(\times\) 3 and & & \\  & & 5 \(\times\) 5; Activation: & & \\  & & Hyperbolic tangent, ReLU, & & \\  & & SoftMax; Dropout: 0.2. & & \\ \hline Real-Time Inspection & \multirow{8}{*}{PointNet++} & Epoch: 500; Batch size: 32; & \multirow{8}{*}{Images: 1500} \\  & & & Training time: 51 h; & \\  & Fasteners Based on Point & & LR: default with default & \\  & Cloud Deep Learning [61] & & decay rate. & \\ \hline UAVs in Rail Damage & \multirow{8}{*}{PointNet++} & \multirow{8}{*}{PointNet++} & \multirow{8}{*}{Images: 1500} \\  & & & & \\  & Image Diagnostics & \multirow{8}{*}{Convolutional Network} & \multirow{8}{*}{13 layers: 15 convolution, RES: 4096 \(\times\) 2160} \\  & & & & \\  & & & \\  & & & \\ \hline Unsupervised Saliency Detection of Rail Surface Defects Using Stereoscopic Images [60] & \multirow{8}{*}{GRNNR} & \multirow{8}{*}{13 layers: 15 convolution, RES: 4096 \(\times\) 2160} \\  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._