# A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
## 3 Road Damage Detection
### One-Stage Detectors for Road Damage Analysis
The approach yields promising results on this specific dataset, indicating the potential for generalization to the original dataset [41]. A different approach is the work from [43], where the innovation lies in their use of image tiling--a technique that breaks down high-resolution images into smaller, standardized patches suitable for YOLO processing--to effectively integrate high-resolution images within the dataset. They employed an ensemble of twelve YOLOv5 models to detect four types of road damages, with their methodology focusing on adapting the image input size and optimizing the model using various data augmentation strategies, including flips, scaling, and color adjustments. Based on ensemble learning, the authors from [44] are using multiple approaches by taking YOLOv5 as the baseline: the first one considers training on the images from all countries, the second one uses multiple models where each is trained on the images from a specific country, the third one uses new anchor boxes for training the YOLOv5, and the fourth one creates various ensemble models by combining the previous three approaches. In [45], the authors present the model YOLOv5s-DSG, focused on addressing the challenges of detecting small-scale road damages under varying resolutions. The model innovates by optimizing both the depth and width of the YOLOv5s architecture to minimize the impact on detection performance while reducing computational overhead. A critical innovation is the incorporation of the Ghost module, which substitutes traditional convolution operations to decrease the model's parameter count, thereby rendering the model more lightweight and increasing its processing speed. Furthermore, the introduction of the Space-to-depth-Conv module is specifically designed to enhance the model's capability in detecting low-resolution and small objects, a common challenge in road disease detection scenarios. In [46], the authors propose another variation starting from a baseline YOLOv8 [33] model, BL-YOLOv8. Central to this model is the adoption of a reconstructed neck structure, incorporating the BiFPN, a move aimed at diminishing the model's parameters and computational complexity. A notable innovation is the introduction of the LSK-attention mechanism, a dynamic large convolutional kernel attention strategy devised to broaden the model's receptive field, thereby boosting the \(P\) of OD. Additionally, the model integrates a SimSPPF module within its feature pyramid layer, a strategic enhancement designed to expedite the model's operational speed. Through these methodological advancements, L-YOLOv8 achieves a 3.3% improvement in \([EMAIL_ADDRESS]\) over the baseline YOLOv8 model, alongside a remarkable reduction in both parameter volume by 29.92% and computational load by 11.45%. Other remarkable results have been obtained by working with other datasets. For example, in [47], the authors labeled 665 Brazilian highway images with two classes, cracks and potholes. Using data augmentation, they deployed YOLO to identify these road damages and obtained a 91% defect detection rate. In the same manner of working with YOLO, the authors from [48] work with a Spanish roads dataset of 600 images with two labels, potholes, and cracks. With some image augmentation techniques, they tested three types of YOLO models with promising results. Another approach based on YOLO is the work from [49], where the authors propose CrackYOLO, a model based on YOLOv5, incorporating several innovative crack detection modules. Their tests are performed on a dataset consisting of 9801 rural roads with three main categories of cracks as labels: transverse cracks, longitudinal cracks, and reticular cracks. In [50], the authors collected 2900 images from the Fengtai District of Beijing, labeling seven classes: longitudinal cracks, transverse cracks, alligator cracks, potholes, manhole covers, longitudinal patch, and transverse patch.