# A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
## 3 Road Damage Detection
### Two-Stage Detectors for Road Damage Analysis  
The evolution from R-CNN [51] to Faster R-CNN [52] has notably advanced the field of RDD, providing a nuanced approach to identifying road damages. Unlike their one-stage counterparts, the two-stage detectors generate region proposals and then classify those regions into object categories in the second stage. This hierarchical method could enhance the accuracy of detecting various road damages by meticulously examining potential areas of interest before classification, thereby offering a more precise and reliable analysis of road conditions. Using the dataset from [8], the authors from [53] employed the Faster R-CNN framework to analyze aspect ratios and sizes of damaged areas in the dataset. The core of their methodology involves the utilization of two variants of the ResNet architecture, ResNet-101 and ResNet-152, as the backbone for feature extraction within the Faster R-CNN model. This choice is pivotal to their approach, as ResNet architectures are known for their deep residual learning framework, enabling the training of much deeper networks by addressing the vanishing gradient problem often encountered in such scenarios. A different approach, but on the same dataset, is the method proposed in [54]. DenseNet [24] is the backbone for feature extraction within Mask R-CNN, a method that promises improvements for the model's efficiency in learning necessary features for accurate detection. This novel combination leverages the strength of DenseNet in feature extraction, characterized by its dense connectivity that ensures maximum information flow between layers. By employing DenseNet as the backbone for Mask R-CNN, the study not only enhances the model's capability in distinguishing subtle features of road damage but also improves the model's efficiency by reducing the number of parameters compared to traditional architectures. Another approach has been developed, starting with the RDD 2020 dataset from [29]. In [55], the authors investigate the application of Detectron2's [56] implementation of Faster R-CNN across various configurations and base models. Their methodological innovation lies in evaluating Detectron2's Faster R-CNN with an X101-FPN base model. The study achieves \(F1\) scores of 51.0% and 51.4% on two distinct test sets, indicating a promising direction for further refining the balance between \(P\) and \(R\) in RDD tasks. A different approach is presented in [57] where, at the beginning, the authors classify the geographic origin of each image by employing a two-step process. Initially, a Country-Classifier, developed through the adaptation of Faster R-CNN, determined the image's country of origin by labeling the entire image with a country-specific bounding box. Subsequently, the image was directed to a specialized regional expert Faster R-CNN model, finely tuned to detect and classify road damages specific to that country. An interesting method is presented in [58], where the authors fine-tune the Faster R-CNN architecture, employing ResNet and Feature Pyramid Networks (FPN) as backbones to enhance the model's detection capabilities. They conduct a thorough analysis to identify the most effective configurations, comparing the performance of models with different backbone networks like ResNet-50 and ResNet-101. The experiments reveal insights into the impact of various hyperparameters and training strategies on the model's ability to generalize across countries with diverse road conditions. In [59], the authors propose to integrate a consistency filtering mechanism with the Cascade R-CNN model, aimed at enhancing the performance of OD systems in identifying RDD from images. Their methodology leverages unlabeled data through self-supervised learning, employing high-confidence samples with pseudo-labels for training and applying various data augmentation techniques to improve model robustness. The study emphasizes using advanced segmentation methods and model ensembling through weighted box fusion to refine the final predictions.