# A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
## 3 Road Damage Detection
### Discussions
For instance, one can observe that while SSD-Mobile Net boasts a high \(P\), it may not be the most comprehensive metric to consider, as models with high \(F1\) scores, like YOLOv7, suggest a better balance between \(P\) and \(R\), which is vital in practical scenarios where both false positives and false negatives carry significant costs. Generalizing this idea, there is a clear predilection towards YOLO variants for achieving high \(F1\) scores, indicative of a balanced \(P\) and \(R\). This is contrasted by the deployment of Faster R-CNN models, preferred for their meticulous localization capabilities, essential for detailed damage assessments. The emergence of specialized models, such as CrackYOLO and CrdNet, tailored to specific damage types or conditions, highlights the trend towards optimizing architectures for enhanced performance on specialized datasets. Ensemble models that integrate the strengths of YOLO and Faster R-CNN \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Work** & **Architecture** & **Metric** & **Value** & **Dataset** \\ \hline
[36] & YOLO-LRDD & F1 & 0.59 &
\begin{tabular}{l} RDD2020 + new Chinese road \\ damages \\ \end{tabular} \\
[38] & E-EfficientDet & F1 & 0.47 & RDD-2020 \\
[40] & YOLOv5 & mAP & 0.55 & RDD-2020 \\
[42] & YOLOv7 & F1 & 0.66 & RDD-2022 \\
[43] & YOLOv5x with ensemble approach & F1 & 0.67 & RDD-2022 \\
[44] & YOLOv5x with ensemble approach & F1 & 0.73 & RDD-2022 \\
[45] & YOLOv5s-DSG & mAP@50 & 0.63 & RDD-2022 \\
[46] & BL-YOLOv8 & F1 & 0.87 & RDD-2022 \\
[47] & YOLO & P & 0.91 & BrazilDNIT \\
[48] & YOLOv7 & mAP@50 & 0.73 & UAV Spanish roads \\
[49] & CrackYOLO & mAP & 0.70 & Rural roads dataset \\
[50] & YOLOv5s & F1 & 0.67 & Beijing dataset \\
[53] & Faster R-CNN with ResNet-152 as & F1 & 0.63 & RDD-2018 \\ backbone & & & & \\
[54] & Mask R-CNN with DenseNet as & Visual & - & RDD-2018 \\ backbone & & results & & \\
[55] & Faster R-CNN with X101-FPN base & F1 & 0.51 & RDD-2020 \\ model & & & & \\
[57] & Faster R-CNN & F1 & 0.49 & RDD-2020 \\
[58] & Faster R-CNN with ResNet-50 & F1 & 0.54 & RDD-2020 \\
[59] & Cascade R-CNN with CFM & F1 & 0.63 & RDD-2020 \\
[61] & Faster R-CNN & Visual & - & Cracks dataset \\  & results & & & \\
[62] & CrdNet & mAP & 0.91 & China roads dataset \\
[64] & Faster R-CNN & Acc & 0.98 & Bangladesh roads dataset \\
[60] & Ensemble approach of YOLO and & F1 & 0.77 & RDD-2022 \\  & Faster R-CNN & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._architectures demonstrate superior \(F1\) scores, suggesting that a collaborative approach may yield the best results in applications requiring both rapid detection and accuracy. The various studies on RDD also reveal insightful trends about the datasets used, highlighting their pivotal role in developing and benchmarking neural network models. Table 3 presents the datasets used in RDD, comparing them by size, number of classes, and the countries where the images were acquired. Notably, the RDD Challenge with the datasets RDD-2018, RDD-2020, and RDD-2022 emerge as popular choices, underscoring their comprehensive nature and relevance in capturing a wide array of road damage types, likely making them benchmark datasets in the field. The appearance of specialized datasets, such as the AerialCrackDataset and those focusing on specific geographic regions (e.g., Brazilian highways, rural roads, and UAV images of Spanish roads), points to the field's evolving need for diverse datasets. Figure 8 presents a pie chart reflecting the distribution of the dataset used in RDD. Figure 8: Distribution of the datasets used in Road Damage Detection (RDD). Figure 7: Distribution of the used Convolutional Neural Network (CNN) in Road Damage Detection (RDD). Thus, multiple challenges appear that reflect the varied conditions encountered in real-world scenarios. These diverse datasets facilitate the tailoring of neural network architectures to specific detection tasks, enhancing their robustness and adaptability across different environmental conditions and damage types.