# Productivity Assessment of the Yolo V5 Model in Detecting Road Surface Damages
## 5 Performance Evaluation of Object Detection Models
### Performance Evaluation Using a Single Data Source  
#### 5.3.1 Model Performance Based on Rising mAP and AR Values  
The mAP (mean average precision) value is a measurement used to evaluate the performance of object detection models. It is often used to compare and evaluate the accuracy of different models.  
In the object detection problem, mAP measures the ability to properly classify and accurately locate objects. The model is evaluated based on calculating the accuracy of the proposed bounding boxes compared to the actual bounding boxes in the test dataset.  
Figure 8: The model’s running speed during testing with a resolution of 640 \(\times\) 640.  
The mAP value is calculated by calculating the average precision of a series of precision-recall values for different thresholds. A high mAP value indicates that the model has good object detection and positioning capabilities.  
Comparing mAP values between models allows us to identify which model has better performance in object detection in test datasets. Models with higher mAP values are generally considered to have better performance and are preferred for use in practical applications.  
In this research, all the models underwent training and validation using the dataset provided by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] (2016). A summary of the validation outcomes for the eight models and the Yolo V5 model is displayed in Tables 2 and 3 and Figures 9-12:In relation to the enhancement of AP values illustrated in Table 3, all complete-scale detection models implementing the Faster R-CNN detector outperformed those employing the SSD detector. However, an entirely distinct outcome was observed with Yolo V5.  
Among the listed models, Faster R-CNN Inception ResNet V2 displayed the most noteworthy average mAP score at 27.66%, trailed by Faster R-CNN ResNet 50 and Faster R-CNN Inception V2, which achieved mAP values of 27.35% and 26.45%, correspondingly. In contrast, the SSD detection models consistently demonstrated lower mAP scores (20%). Interestingly, the Yolo V5 model, utilizing the SSD detector, yielded an exceptional mAP score of 73.02% and achieved the highest mean at 75.61%, which was quite surprising.  
Figure 10: mAP 0.5:0.95.  
Figure 9: mAP 0.5.  
Table 4 provides a comprehensive overview of the performance results for all models concerning each damage type, evaluated using an Intersection over Union (IoU) threshold of 0.5. The outcomes reveal that Faster R-CNN Inception ResNet V2 stands out as the most proficient model in identifying three out of the five damage types (I, II, and III), showcasing AP values of 71.64%, 45.28%, and 70.77%, respectively. Faster R-CNN Inception V2 excels in detecting type V (blurring), achieving an AP value of 76.59%. Additionally, it matches the performance of Faster R-CNN Inception ResNet V2 in detecting type I, with an AP of 71.29%. Conversely, Faster R-CNN ResNet 50 achieves the highest AP value (12.23%) for identifying type IV damage. In contrast, the Yolo V5 model displays exceptional performance across all types of road surface damage, a feat rarely accomplished by other models. Notably, Yolo V5 SSD exhibits relatively lower performance, especially in the detection of type IV, with a value of 70.65%.  
Figure 11: Precision.  
Figure 12: Recall.