# RDD-YOLO: Road Damage Detection Algorithm Based on Improved You Only Look Once Version 8
## 2 Related Work
### YOLO Algorithm  
The development of object detection can be classified into two directions: one-stage detection algorithms and two-stage detection algorithms. As illustrated in Figure 1, two-stage detection algorithms generate region proposals after feature extraction and are followed by classification and regression operations on the region proposals to produce results. In contrast, one-stage detection algorithms do not need to generate region proposals. They directly extract features in the network for classification and regression operations, producing results without an intermediate step. YOLO belongs to the one-stage object detection algorithm category. It transforms the object detection problem into an end-to-end regression problem, predicting all object bounding boxes and categories simultaneously using a single feed-forward neural network. It can detect and locate objects directly from the image in a single forward pass. Figure 2 shows the evolution timeline of the YOLO algorithm. YOLOv1, the initial version of the YOLO algorithm, was introduced by [PERSON] et al. at the University of Washington in 2015 grid [16] and analyzes whether each border is the position and confidence of the detected object [17]. YOLOv1 has a small computational load and fast runtime, but it is less effective in detecting small targets and requires accuracy improvement. In 2017, [PERSON] et al. proposed YOLOv2 [18]. It incorporates batch normalization to improve model convergence, further accelerating training speed. YOLOv2 also introduces anchor boxes to improve the detection capabilities for objects of different sizes. Addition  
Figure 1: Object detection algorithm classification. ally, YOLOv2 can simultaneously detect over 9000 different classes, so it is also called YOLO9000, which is more versatile. In 2018, [PERSON] et al. released YOLOv3 [19], introducing residual networks, spatial pyramid pooling (SPP), and cross-stage partial networks (CSPNet) for multi-scale detection. YOLOv3 deepens the network and achieves progress in small-object detection, multi-object detection, and accuracy. YOLOv4 was released in 2020 by [PERSON] et al. [20]. YOLOv4 essentially consolidates an array of object detection techniques, tests, and enhancements to offer a real-time, lightweight object detector [21]. YOLOv4 introduces mosaic data augmentation, a spatial attention module (SAM), and other technologies, making it more suitable for single general processing unit (GPU) training and showing improvements in speed and accuracy. Shortly after the appearance of YOLOv4, Ultralytics introduced YOLOv5, incorporating adaptive anchor boxes and offering various network architectures for increased convenience and flexibility. It segments the input image into s \(\times\) s grids and predicts the boundary of each. YOLOv5 has a lighter overall model with performance comparable to YOLOv4. YOLOv6, released by Meituan's Vision Intelligence Department in June 2022 [22], introduces techniques such as a bidirectional concatenation (BiC) module, anchor-aided training (AAT) strategy, enhanced backbone and neck design, and self-distillation strategy to further improve the algorithmic performance. YOLOv7 was released a month after the release of YOLOv6 by [PERSON] et al. [23]. It focuses on optimizations of the model structure reparameterization and dynamic label assignment problems, reducing the number of parameters and computations, with faster inference and higher detection accuracy. YOLOv8 was released by Ultralytics in January 2023. YOLOv8 builds on previous versions by introducing an anchorless segmentation head structure and optimizing the backbone and intermediate architectures to improve flexibility, efficiency, accuracy, and performance. YOLOv8 encompasses a comprehensive suite of vision artificial intelligence (AI) tasks, spanning detection, segmentation, pose estimation, tracking, and classification. This flexibility enables users to harness YOLOv8's capabilities across various applications and domains.