# A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
## 3 Road Damage Detection
### Discussions  
As analyzed previously in this section, the advancement of CNN architectures in the problem of RDD has been pivotal. This task refers to detecting and classifying road damages, including but not limited to cracks, potholes, or various surface anomalies. The precise identification of these damages is essential for the operational safety of autonomous vehicles, facilitating secure navigation and avoiding potential dangers. Table 2 provides a systematic overview of various methods applied to this task. It acts as a consolidated reference, detailing the performance metrics, datasets used and the architectural highlights of each proposed solution. The first column reveals the paper reference, the second column is the architecture that obtained the best metric value reported in the work, column 3 reveals the used metric, while column 4 underlines the metric value. The last column presents the dataset for which the architecture was trained and tested. From the neural architectures point of view, the most used CNNs are the one-shot detectors, especially the YOLO family of models and their subsequent versions. These characteristics make them well-suited for real-time applications where rapid and reliable RDD is critical. An alternative to this is the SSD architecture, which also promises good speed and _Acc_ but not better performance in detecting small objects compared to YOLO. Given that this aspect can be critical in detecting cracks or potholes, YOLO could be preferred due to its advantageous processing of the entire image at once, allowing it to recognize context and reduce false positives effectively. The Faster R-CNN family emerges as a notable alternative, particularly for its precision in detecting a wide range of damage sizes. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Work** & **Architecture** & **Metric** & **Value** & **Dataset** \\ \hline
[8] & SSD-Mobile Net & P & 0.99 & RDD-2018 \\
[13] & RetinaNet with VGG19 as backbone & mAP & 0.83 & RDD-2018 \\
[15] & YOLOv3 & F1 & 0.62 & RDD-2018 \\
[16] & Ensemble (16 models) & F1 & 0.66 & RDD-2018 \\
[19] & Yolov4 with PID optimizer & mAP & 0.47 & RDD-2018 \\
[19] & Yolov3 with improvements & mAP@50 & 0.52 & AerialCrackDataset [21] \\
[22] & Yolov4 with PID optimizer & mAP & 0.63 & RDD-2018 \\
[23] & DenseNet & F1 & 0.75 & EDDmCrack1000 \\
[26] & EfficientDet & F1 & 0.56 & RDD-2020 \\
[31] & YOLOv4 with ensemble approach & F1 & 0.64 & RDD-2020 \\
[32] & u-YOLO with ensemble approach & F1 & 0.67 & RDD-2020 \\
[34] & Ensemble model based on YOLOv3 & F1 & 0.57 & RDD-2020 \\  & and Faster R-CNN & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summarization of state-of-the-art road damage detection methods. Unlike YOLO and SSD, which excel in speed and are adept at handling smaller objects to an extent, Faster R-CNN is distinguished by its two-stage process, where it first generates region proposals and then classifies those regions into object categories. This methodical approach allows for more accurate detection, making Faster R-CNN highly effective in scenarios where the precision of damage identification outweighs the need for real-time processing. Other used architectures are DenseNet and EfficientDet, each bringing their contributions to the field. DenseNet's architecture facilitates feature reuse throughout the network, which can be advantageous for identifying the intricate features of road damage. EfficientDet, on the other hand, is designed for efficiency, scaling systematically with the dataset and task complexity. Interestingly, the table also includes novel architectures like CrackYolo and CrdNet, tailored specifically for the task of RDD. These specialized networks potentially offer optimizations over more general architectures and reflect the ongoing innovation in the domain. A visual overview of the most used neural networks in RDD is shown in Figure 7.  
Regarding the most used metrics from the RDD state-of-the-art analysis, see Tables 1 and 2; multiple comments can be made.