# Improved YOLOv5-Based Real-Time Road pavement Damage Detection in Road Infrastructure Management
## 4 Result Analysis
### Evaluation Metric  
This research utilizes various evaluation metrics, including precision, recall rate, F1 score, mean Average Precision (mAP), number of parameters, and GFlops (i.e., One billion Floating-Point Operations per second). The precision metric of a model determines the proportion of correctly predicted positive labels compared to the overall number of predicted positive labels. The precision formula is presented in Equation (16).  
\[Precision\ Score=\frac{TP}{(FP+TP)} \tag{16}\]  
Recall is a metric that evaluates the accuracy of a model's ability to predict positive outcomes based on actual positive results, in contrast to other performance metrics. The metrics of True Positive (TP), False Positive (FP), and False Negative (FN) are employed in the calculation of precision and recall. The Recall equation is provided in Equation (17).  
\[Recall\ Score=\frac{TP}{(FN+TP)} \tag{17}\]  
The F1 score is a metric that evaluates a model's effectiveness by considering both its precision and recall scores. The F1 score is a performance metric that assigns equal importance to Precision and Recall when assessing the effectiveness of a machine learning model. The technical expression for this is the harmonic mean of the precision and recall scores. The mathematical expression for the F1 score is presented in Equation (18).  
\[F1\ Score=\frac{2*Precision\ Score*Recall\ Score}{(Precision\ Score+Recall\ Score)} \tag{18}\]  
The Mean Average Precision (mAP) is a typically used metric for assessing object detection models. Initially, the precision and recall metrics are calculated for every detection  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hyper Parameter** & **Value** \\ \hline Optimizer & SGD \\ Epochs & 100 \\ Batch size & 16 \\ Image Size & 640 \(\times\) 640 \\ workers & 6 \\ Auto Anchor & False (Default = True) \\ Label\_smoothing & 0.1 (Default = None) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training configuration of our improved YOLOv5s.  
Figure 7: Different classes of this study and their quantity.  
within the category across a spectrum of confidence threshold values. Subsequently, the mean Precision is determined by utilizing the region beneath the class's precision-recall curve (AUC). Upon computation of the AP score for every class, the mAP score is derived using the average of these individual scores. The mAP score is a significant metric that provides a comprehensive evaluation of the detection performance of the model across all categories in the dataset. The mathematical expression for mAP is presented in Equation (19).  
\[mAP=\frac{1}{n}\sum_{k=1}^{k=n}AP_{k} \tag{19}\]  
where \(AP_{K}\) = the \(AP\) of class \(k\) and \(n\) = the number of classes.  
GFlops are employed for assessing the efficacy of a model or algorithm. Typically, a lower GFlops value corresponds to reduced computational demand for model rendering, lower hardware performance requirements, and increased feasibility for implementation in low-end devices. Measuring parameters is crucial in assessing a model's complex nature and computational requisites. A model's complexity is directly proportional to its number of parameters. A more significant number of parameters signifies the complexity of a model, which could require more computational resources for training and deployment. Hence, it is essential to evaluate the efficacy of object detection models by examining their parameter count and performance on relevant metrics such as mAP, Precision, and Recall.