# Improved YOLOv5-Based Real-Time Road pavement Damage Detection in Road Infrastructure Management
## 2 Related Work  
The employment of methods based on deep learning has gained substantial acceptance because of their remarkable efficacy across a wide range of domains, such as computer vision, soil science, and solar flare prediction [13]. The transportation industry is subject to the effects of technological advancements. The rapid progress and extensive implementation of deep learning technologies can potentially enhance the ability to detect road pavement damage. According to current studies, maintaining road infrastructure is essential for establishing a secure, economically feasible, sustainable transportation system. Therefore, researchers are now aiming to utilize techniques such as deep learning to evaluate road pavement damage and optimize cost reduction. Several methodologies based on deep learning have achieved noteworthy advancements. The efficacy of deep learning techniques has catalyzed the creation of additional algorithms that rely on feature extraction and pattern recognition to detect road pavement damage. [PERSON] and [PERSON] [14] proposed using the mean and standard deviation from unsupervised learning to discriminate between road pavement cracks and non-crack blocks. [PERSON] and [PERSON] [15] suggested using the Adaboost algorithm to select structural descriptors that effectively depict fractured images and produce the crack saliency map. Despite the impressive outcomes produced by these techniques, their practical utility is significantly limited by their dependence on extracted features. The detection of objects can be achieved through various techniques, with Faster R-CNN and YOLO being two of the approaches most commonly used. The Faster R-CNN approach was utilized by [PERSON] et al. to propose a method for the recognition of road surface damage [16]. The system can discriminate among six distinct categories of road surface defects and accurately pinpoint the precise location of the affected region within a given image. The research employed 5966 photographs, resulting in an average precision rate of 96.3%. Researchers use two distinct image types for road pavement damage detection study. One is a snapped photo of the top view, while the other is of the front perspective. It is described in Figure 1. Top-view photographs are acquired from the road's surface, while a dashboard-mounted camera captures front-view images. Top-view damage photos are less complicated, and most studies on top-view damage have excellent detection accuracy. However, most of these models solely exhibit damage caused by cracks. Most studies based on top-view images did not attempt to classify different types of road pavement damage. [PERSON] et al. [17] used a Bayesian technique to optimize CNN design to maximize the abstraction of the final model with a 97% percent accuracy rate. In their study, [PERSON] et al. [18] conducted fine-tuning of the filter sizes in a Convolutional Neural Network (CNN) to attain a satisfactory accuracy rate of 94.5% in determining various types of cracks in road photographs. The authors' findings suggest that this approach may be effective in road maintenance and inspection. The majority of research about top-view images is centered around semantic segmentation. [PERSON] et al. [19] utilized the U-Net architecture to identify cracks in concrete. The model that has undergone training exhibits a high degree of accuracy in identifying cracks present in images. FCNs can be outperformed by this approach, as it requires a smaller number of training sets to achieve superior results. [PERSON] et al. achieved a recall rate of 97% through the utilization of diverse image processing techniques on the UNET model, as reported in their study [20]. [PERSON] et al. [21] developed a CrackNet architecture that does not incorporate a pooling layer to detect cracks on roads. The model achieved a high level of accuracy, specifically 90.1%. The model exhibited impressive results by attaining a precision of 98.00% and an accuracy of 97.92% in the classification of various types of crack damage.