# Productivity Assessment of the Yolo V5 Model in Detecting Road Surface Damages
## 2 Literature Review of Road Damage Detection  
Among the studies published on road damage detection and classification using machine learning and deep learning methods, [PERSON], [PERSON], [PERSON] and [PERSON] [2] reported that they could be categorized based on the type of road images used. Many researchers have successfully applied deep learning models to classify damage in manually captured road surface images with relatively accurate results. For instance, [PERSON] et al. [22] established a convolutional neural network model using 500 images to determine whether there are cracks in road images. [PERSON] et al. [23] and [PERSON] et al. [24] built a CrackNet architecture without a pooling layer to classify whether there are cracks on asphalt concrete road surfaces with an accuracy of 90.1%. Meanwhile, the model by [PERSON] et al. [25] included 1200 collected road surface images, and a dataset containing corresponding labels of defects was established [PERSON] et al. [26]. Based on this dataset, the performance of Mask R-CNN was compared with faster region-based convolutional neural networks (Faster R-CNN). Meanwhile, the testing time for models using Feature Pyramid Networks (FPN) was lower than other models, achieving 0.21 s per frame (SPF). However, most of these models had relatively small training datasets, and further improvements are needed to distinguish a variety of road surface crack types. The second group focusing on road damage classification and detection primarily aims to establish models for image-based detection using cameras mounted on vehicles, such as dashcams and smartphones [PERSON] et al. [27]. [PERSON] and [PERSON] [28] developed a novel pothole detection algorithm that operates within an embedded computing environment. It processes video feeds from dash cameras, enabling real-time and accurate detection of potholes. [PERSON] and [PERSON] [29] applied Fully Convolutional Networks (FCN) with semi-supervised learning to detect road damage on images captured by dashboard cameras. They used image segmentation and brightness adjustment to enhance the results. [PERSON] et al. [30] integrated Faster R-CNN with Structured Random Feature Enhancement Detection (SRFED) to detect cracks on structural surfaces. In a recent study, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [16] proposed the use of SSD Inception V2 and SSD MobileNet to detect and classify road damage into eight categories, achieving an accuracy rate of 77% and a recall rate of 71%. While these results may not meet modern dataset standards, they have led to a decent average precision (AP) score. However, the datasets used in these studies were collected through dash cameras, making it challenging to quantitatively determine the actual size and area of damages. Thus, future work should address three key aspects: image acquisition perspective, dataset augmentation, and the exploration of more algorithms to further improve road damage detection efficiency. The Fourth Industrial Revolution has introduced various technological tools that have significantly enhanced multiple fields. [PERSON], [PERSON], [PERSON], Tian, [PERSON] and [PERSON] [22] reported that one of the prominent machine learning software platforms is Yolo V5 (2023) [PERSON], [PERSON], [PERSON], Tian, [PERSON] and [PERSON] [22]. Yolo (You Only Look Once) is known for its efficiency in machine learning-based object detection, as stated by [22]. The Yolo V5 software, being the fifth generation of Yolo, offers an effective tool for developing software that can detect objects in images related to technical infrastructure [PERSON], [PERSON], [PERSON] and [PERSON] [23]. This advancement in Yolo V5 allows the development of software with superior accuracy and detection speed compared to earlier platforms. Scientific research in the field of information technology always demands novelty and staying updated with trends. One of the important requirements for researchers is to establish the basis for comparing research results, often relying on evaluations from prior studies.