# Productivity Assessment of the Yolo V5 Model in Detecting Road Surface Damages
## 3 Presenting the Yolo V5 Network Architecture Model and Introducing Other Detection Models  
### Overview of Road Surface Damage Image Detection Models  
In recent years, improvements in image processing techniques (ITPs) have spurred significant improvements in automatic pavement crack detection models. In these models, precise and rapid analyses of two-dimensional (2D) images of road pavement are conducted to extract and assess useful features for yes/no crack detection and to tag appropriate images for further analyses [PERSON] et al. [31]. Therefore, this study was designed to comprehensively assess the performance and capability of the recently developed deep learning-based state-of-the-art detection models to detect various types of damage to asphalt roads, including longitudinal cracks, horizontal cracks, potholes, and alligators [PERSON] et al. [32]. The models' performance is assessed based on results obtained from implementing detection tasks on many image sources with different sizes [PERSON] et al. [33; 34].  
This part of the study will delineate the primary constituents of object detection software: the model for detecting objects and the foundational structures of deep learning employed in extracting features from images. Initially, this research will showcase two models for object detection: the Faster RCNN, a two-stage model, and the SSD model (Single Shot Multi-Box Detection), a one-stage model. Subsequently, it will introduce four crucial deep learning structures used to extract features--MobileNet, Inception, ResNet, and Inception ResNet, Google LLC, Mountain View, CA, USA. Ultimately, the investigation will cover eight detection models formed by amalgamating the object detection model with the feature extraction architectures (Figure 1). The aim is to identify objects in images and subsequently contrast the performance of these models.  
1.1 Two Object Detection Models Are Faster RCNN and SSD in the Latest Object Detection in Images Technology Today  
This study briefly introduces the working process of the two newest object detection models, including Faster-RCNN models (two-stage model) and SSD models (Single Shot Multi-Box Detection) (one-stage model). In general, Faster RCNN is a two-stage model, in which the first stage is to generate region proposals using the RPN network and the second stage is to classify and improve the position. Next, SSD is a one-stage model that predicts bounding boxes and classifies directly on grid cells on specific feature maps [8; 35].  
Faster RCNN (two-stage model): The workflow of Faster R-CNN (Faster Region-based Convolutional Neural Network) includes two main modules: Region Proposal Network (RPN) and R-CNN (Region-based Convolutional Neural Network) (Figure 2).  
Figure 1: The formation methods of the 8 detection models.  
Figure 2: Working process of Faster R-CNN.  
Region Proposal Network (RPN): RPN is a convolutional neural network used to recommend candidate regions that are likely to contain objects in an image. RPN predicts candidate bounding boxes and calculates confidence scores for each box. Boxes with high confidence scores are suggested as candidate regions to contain objects. RPN uses loss functions to optimize the correct bounding box prediction and ensure the diversity of candidate regions.  
R-CNN (Region-based Convolutional Neural Network): R-CNN is a convolutional neural network used to classify and accurately predict the position of objects in each candidate region proposed by RPNs. R-CNN extracts features from each candidate region and passes them through fully connected layers to classify objects and adjust the position of bounding boxes. R-CNN also uses loss functions to optimize classification and position prediction.