# A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
## 3 Road Damage Detection
### One-Stage Detectors for Road Damage Analysis  
The influence of one-stage detectors in the field of RDD has been transformative, marking a significant shift towards more efficient methods. Characterized by their ability to perform OD in single-pass, one-stage detectors like YOLO (You Only Look Once) [9] or SSD (Single Shot Multibox Detector) [10] have streamlined the process of identifying road damages. An example in this sense is the work from [8] where the authors evaluate two specific models: SSD using Inception-V2 [11] and SSD using MobileNet [12] frameworks. These models were chosen for their balance of accuracy and efficiency, which is particularly important for real-time processing on devices with limited computational resources. In terms of performance, the work reveals the finding that SSD using MobileNet demonstrates superior results compared to the usage of Inception V2. This idea is sustained by computing high Recall (R) and Precision (P) in detecting various types of road damage in detecting longitudinal, lateral, and alligator cracks. The research also delves into the inference speed of the models, an essential factor for real-time applications. It is noted that the SSD MobileNet model could process data in 1.5 seconds on a smartphone, enabling the potential for real-time damage detection in moving vehicles. Another pivotal aspect is the development of a large-scale dataset of road damage images captured through a \begin{table}
\begin{tabular}{l l c} \hline \hline
**Metric** & **Description** & **Formula** \\ \hline Precision (P) & The ratio of correctly predicted positive & P = \(\frac{\text{TP}}{\text{TP}+\text{TP}}\) \\  & observations to the total predicted positives. & \\ Recall (R) & The ratio of correctly predicted positive & R = \(\frac{\text{TP}}{\text{TP}+\text{TN}}\) \\  & observations to the total actual positives. & \\ Average Precision & Sum of products of P at each R threshold and the & AP = \(\sum_{n}(\text{R}_{n}-\text{R}_{n-1})\text{P}_{n}\) \\ (AP) & change in R from the previous threshold. & \\ mAP & Mean of the average precision scores across & mAP = \(\frac{\text{1}}{\text{N}}\sum_{i=1}^{N}\text{AP}_{i}\) \\  & different classes or queries, averaging the area & \\  & under the precision–recall curve. & \\ F1 Score & Harmonic mean of precision and recall. & F1 = \(2\times\frac{\text{P}.8}{\text{TP}+\text{R}}\) \\ mAP@50 & mAP calculated specifically at an Intersection over & mAP at IoU \(\geq\) 0.50 \\  & Union (IoU) threshold of 0.50. & \\ Accuracy & Ratio of correctly predicted observations to the & Acc = \(\frac{\text{TP}+\text{TN}}{\text{Total samples}}\) \\  & total observations. & \\ \hline \hline \end{tabular} Note: TP: True Positives; FP: False Positives; TN: True Negatives; FN: False Negatives.  
\end{table} 
Table 1: Most used performance metrics. Figure 6: Example of Road Damage Detection (RDD) detection from [8]. smartphone, which includes 9053 images with 15,435 instances of road damage across eight categories ([8]). By working with the same dataset, the authors from [13] use RetinaNet [14] as the one-stage detector. The innovative core of their methodology lies in the application of RetinaNet's focal loss function, which significantly improves the model's performance on class imbalance--a common issue in this dataset where damaged areas are sparse compared to the undamaged. In the same manner, the authors from [15] utilized the YOLO network, with variations in the number of training iterations, minimum confidence threshold, and non-maximum suppression. Through the last one, they achieved the best results by obtaining an \(F1\) score of 0.62. In comparison with the solutions above, the authors from [16] revolve around the application of RDD by the usage of SSD network enhanced with the pre-trained networks VGG [17] and Residual Networks (ResNet) [18], which provide robust feature extraction capabilities.