# Improved YOLOv5-Based Real-Time Road pavement Damage Detection in Road Infrastructure Management
## 2 Related Work
While the models mentioned above exhibit remarkable detection capabilities, their practical implementation is restricted. The acquisition of such road images through specialized vehicles regularly incurs substantial costs. The road damage detection model using front-view images focuses on developing detection and classification models employing images captured by dashboard-mounted cameras. Identifying road pavement damage through automated means is a viable and pragmatic approach in real-world scenarios, with extensive ongoing research being conducted in this domain. However, the procedure is considered challenging due to the complex and obscure nature of the damage in this particular type of image. Furthermore, apart from the road surface, the contextual elements captured in these photographs include environmental factors such as atmospheric conditions, geographical features, and other noise, which pose challenges to identifying pavement damage. Nevertheless, effectively utilizing these images for damage detection would yield significant benefits in pavement monitoring activities. Currently, the repositories of front-view images are extensive. Furthermore, it has the potential to be swiftly and effortlessly augmented. Again, utilizing comprehensive pavement coverage images during inspections and camera installation procedures is simple and cost-effective. Despite the numerous benefits of using images as a resource, studies in this area remain limited due to images' diverse and complex textures. Deep learning-based models have been proposed as a viable solution to tackle these challenging requirements effectively [22]. However, only a few models have their performance evaluated using images taken by dashboard-mounted cameras. [PERSON] et al. [23] proposed a lightweight semantic segmentation method. This is noteworthy, as most research on front-view images typically focuses on object detection. Their proposed approach enhances precision, decreases parameters by 85.7%, and improves computational speed by 6.1%. Furthermore, they enhanced the model's parameters without considering any potential impact on its detection speed. They proposed additional research utilizing a lightweight auto-encoder network with a hierarchical layout to detect pavement damage [24; 25]. Most of these models did not attempt to categorize damage into distinct classifications. Instead, researchers employ object detection models, such as Faster R-CNN, YOLO, and SSD, to identify and categorize instances of pavement damage. [PERSON] et al. [26] proposed combining a Single shot detector (SSD) with an Inception backbone and an SSD with a MobileNet backbone to detect and classify eight categories of pavement damage with a 71% Recall score. [PERSON] et al. [27] proposed a model based on the YOLOv5x architecture, which yielded an F1 score of 67%. However, the detection speed could be more satisfactory. [PERSON] et al. [28] identified and classified pavement damage with a 62.5% F1 score using a Faster R-CNN model with data augmentation techniques. [PERSON] et al. [29] employed the Mobilenetv3 architecture as the backbone and trained YOLOv5s using the RDD 2020 dataset. The algorithm utilized by the authors integrates coordinate attention, K-means, and label smoothing techniques. Incorporating diverse techniques in their algorithm resulted in a 2.5% enhancement of the F1 score compared to the YOLOv5 baseline model. [PERSON] et al. [30] trained RDD 2020 using ShuffleNetv2 as the backbone and ECA-Net as the attention mechanism in YOLOv5s. They use Focal Loss and BiFPN neck instead of YOLOv5's original neck. Their model increases the efficiency of image recognition by 22.3%. Recently, [PERSON] et al. [31] employed the YOLOv7 model with coordinate attention and label smoothing techniques on the RDD 2022 dataset. In addition, they customize the default image enhancement settings of YOLOv7. This exhibits enhanced capacity for generalization and resilience.