# Improved YOLOv5-Based Real-Time Road pavement Damage Detection in Road Infrastructure Management  
[PERSON]  
1 Department of Computer Science & Engineering, Chittagong University of Engineering & Technology,  
Chottogram 4349, Bangladesh; [EMAIL_ADDRESS] (A.A.S. ); [EMAIL_ADDRESS] (S.S.)  
[PERSON]  
1 Department of Computer Science & Engineering, Chittagong University of Engineering & Technology,  
Chottogram 4349, Bangladesh; [EMAIL_ADDRESS] (A.A.S. ); [EMAIL_ADDRESS] (S.S.)  
[PERSON]  
1 Department of Computer Science & Engineering, Chittagong University of Engineering & Technology,  
Chottogram 4349, Bangladesh; [EMAIL_ADDRESS] (A.A.S. ); [EMAIL_ADDRESS] (S.S.)  
[PERSON]  
1 Department of Computer Science & Engineering, Chittagong University of Engineering & Technology,  
Chottogram 4349, Bangladesh; [EMAIL_ADDRESS] (A.A.S. ); [EMAIL_ADDRESS] (S.S.)  
Footnote 1: email: {k.s. conditions poses a challenge for automated assessment tools. Consequently, the attainment of precision and swiftness becomes increasingly challenging. Additionally, the equipment requires costly hardware, which incurs high maintenance expenses. Image processing techniques can integrate high efficiency while maintaining low cost. As technological advancements are made, the precision of detection is increasing. As a result, many researchers have employed image processing methodologies to identify pavement deterioration [6; 7]. The complexity of road surroundings makes manual feature extraction impractical using conventional image processing techniques. In contrast to traditional image processing methods, deep learning-based image processing techniques have gained widespread adoption to detect pavement damage due to their superior accuracy, speed, and embeddability [8]. Deep learning technology has made significant advancements in detecting road damage. The authors [PERSON] et al. (2019) [9] employed the Faster Region Convolutional Neural Network (Faster R-CNN network) to detect road damage. They additionally evaluated the impact of different lighting and weather conditions on the detection process. A study by [PERSON] et al. [10] presented a novel approach for detecting pavement damage using the one-stage YOLO CSPDarknet53 network. The authors' work contributes to the growing research on the pavement damage dataset, further expanding the range of models explored in this domain. Although the research in pavement damage detection has made progress, there remains significant potential for improvement in accuracy and detection speed. In recent years, the maintenance of roads has become a critical responsibility for traffic management departments due to the rapid expansion of the traffic network. Road pavement damage detection and classification application can be used for various purposes. Some of its applications are Structural Health Monitoring, Autonomous Personal Mobility Vehicles, and Intelligent Transportation Systems (ITS). This study aims to design a deep neural network model that utilizes object detection techniques to identify instances of pavement damage in photographic representations of outdoor road surfaces, as manual inspection is labor-intensive and time-consuming, requiring individuals to capture images to evaluate road damage in real time. Object detectors based on deep learning, such as YOLOv5, have the advantage of efficiently identifying road pavement damage caused by a moving vehicle. This method eliminates the restrictions inherent to manual road inspection. Road pavement damage detection tasks using YOLOv5 are primarily motivated by YOLOv5's impressive accuracy and detection speed as an object detection algorithm. YOLOv5 is one of the most popular and easily customizable object detection algorithms, making it ideal for custom object detection tasks [11; 12]. YOLOv5 proves to be a perfect fit for this study due to its speed and considerable accuracy.