# A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
## 3 Road Damage Detection
### One-Stage Detectors for Road Damage Analysis
They employed an ensemble strategy that involves training multiple instances of the SSD model, each under different configurations and data subsets, by using higher-resolution inputs (\(1024\times 1024\) pixels) and a random cropping technique. The ensemble combines predictions from these models using non-maximum suppression to filter overlapping detections alongside a high confidence threshold to minimize false positives. Additionally, Intersection over Union (IoU) thresholds are applied for refining predictions. Another approach based on the dataset from [8] comes from [19] where the authors used a YOLOv4 [20] model with a Proportional-Integral-Differential (PID) optimizer. This modification aims to address the overshoot problem commonly encountered in DL, thus improving training efficiency. The PID optimizer, integrated into YOLOv4, leverages the derivative term of the PID control theory, focusing on the rate of change of the gradient during model training. This approach leads to faster convergence and reduces training oscillations, effectively improving the model's performance. The modified YOLOv4 model demonstrates superior mean average precision (mAP) compared to the original YOLOv4, with a notable increase of 5.29% (on the dataset from [8]) and 5.22% (on the dataset from [21]). A similar approach is the work from [22], which presents an approach that involves a fusion of low-level features from the early layer of the network with high-level features from the deeper to capture both semantic information and detailed visual characteristics of road damages. Also, they propose a novel loss function that is sensitive to the direction of road crack extension, addressing the common issue of incomplete bounding box predictions. This direction-aware loss function assigns different weights to the \(x\) and \(y\) dimensions of the bounding box, depending on the class of road damage. An approach based on densely connected convolutional layers is presented in [23], where the authors employed a CNN known as DenseNet (Densely Connected Convolutional Network) [24] for automatic pavement crack detection. In their methodology, the authors utilized DenseNet201, a variant of DenseNet with 201 layers, to process images of pavement cracks. A novel aspect of their approach was the introduction of a unique loss function that considers the connectivity of pixels. This function aimed to address the challenges posed by the output of transposed convolution layers, which often lead to sparse and disconnected detections. By focusing on the connectivity aspect, the new loss function helped to generate more coherent and accurate crack predictions. The approach is validated using two datasets: one obtained from a handheld smartphone (CFD dataset [25]) and another one acquired by them from a high-speed camera mounted on a moving car (EdmCrack1000). A different approach is presented in [26], where the authors used EfficientDet [27], a single-stage detector that combines the benefits of EfficientNet (used for feature extraction) and Bi-directional Feature Pyramid Network (BiFPN) [28] for feature fusion. Varying the input image resolution, the batch size, and the learning rate, the paper reports \(F1\)-scores ranging from 52% to 56%. The authors used the dataset from [29], an upgraded version of the dataset from [8], consisting of 26,620 images collected from India, the Czech Republic,and partially from Slovakia, with four damage categories: longitudinal cracks, lateral cracks, alligator cracks, and potholes. On this dataset, the authors from [30] analyzed their dataset using SSD models based on MobileNet architectures. They trained and evaluated 16 different models across 30 scenarios using various data combinations from the three countries. This strategy enhanced the models' generalization capabilities, making them more robust and effective in different settings. In [31], the authors present an innovative approach to RDD by employing a deep ensemble learning model.