# A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
## 3 Road Damage Detection
### One-Stage Detectors for Road Damage Analysis
The paper explores the effectiveness of various YOLO-v4 models trained under different conditions, highlighting the impact of training iterations and image resolutions on detection performance. The ensemble approach, which joins 30 multiple-model predictions, demonstrates notable success, achieving \(F1\) scores of 0.628 and 0.6358 on the dataset. In the same manner of working with an ensemble strategy, in [32], the proposed method is based on adopting ultralytics-YOLO (u-YOLO) [33] as the core model, enhanced through three ensemble learning strategies: ensemble prediction, ensemble model, and a hybrid approach between them. These strategies involve test time augmentation and averaging predictions from multiple trained models, aiming to improve robustness and accuracy. The approaches are evaluated using the dataset from [29], demonstrating an F1 score up to 0.67. The authors from [34] present an approach based on image segmentation and YOLOV4 to identify road damages. The methodology starts with using the DeepLabV3 Plus [35] model for image segmentation to isolate road areas, creating what they refer to as a 'road-interest map'. This map is then used as the basis for training their OD model. A different methodology is proposed in [36], where the authors introduced a novel model for RDD, innovatively enhancing the YOLOv5s framework. Their approach, YOLO-LRDD, incorporates significant modifications such as a new backbone network, Shuffle-ECANet, which combines ShuffleNetV2 [37] with the efficient channel attention module for increased efficiency and speed. The model also uses Weighted BiFPN instead of PANet for better feature fusion and introduces FOCAL-EIOU loss to address sample imbalance and improve anchor box quality. These improvements result in a more lightweight and faster model than the standard YOLOv5 while maintaining a comparable \(Acc\). In the same manner of constructing a lightweight model, the authors from [38] introduce E-EfficientDet, a model based on EfficientDet [27]. They integrate a feature extraction enhancement module, utilizing semi-dense connectivity in its feature pyramid, focusing on improving multi-scale feature extraction and information reuse across different network layers. By employing a compound scaling strategy, E-EfficientDet offers scalable solutions that adjust to varying hardware capabilities, ensuring efficient deployment on both high-end and mobile devices. The model outperforms existing benchmarks, like YOLOv5s and YOLOv7-tiny [39], in \(Acc\) and speed, demonstrating a 2.41% increase in detection \(P\) over the original EfficientDet-D0. An interesting approach is presented in [40], where the authors proposed a multi-faceted system design, integrating roadside data collection, edge computing, and cloud-based data transfer to achieve real-time road damage visualization on client devices. The deployment on the NVIDIA Jetson NX platform (NVIDIA, Santa Clara, CA, USA), optimized with NVIDIA's TensorRT and DeepStream technologies, showcases significant advancements in processing speed and detection accuracy. Experimental outcomes demonstrate the system's capability to accurately and swiftly identify road damages, outperforming previous YOLO versions. Based on the RDD2020 dataset from [29], the same authors examined an upgraded dataset, RDD2022 [41], with 47,420 road images from six countries: Japan, India, Czech Republic, Norway, the United States, and China. The annotations are based on four types of road damage: longitudinal cracks, transverse cracks, alligator cracks, and potholes. In [29] it is presented the evolution of the dataset. Starting from this dataset, the authors from [42] adopt an interesting approach by utilizing YOLOv7, enhanced with coordinate attention to better capture spatial features of the road damages. Their methodology is distinguished by the use of Google Street View for extensive data across the United States to collect the coordinate attention, which was integrated with the neural model.