# RDD-YOLO: Road Damage Detection Algorithm Based on Improved You Only Look Once Version 8
## 4 Experimental Results and Analysis
### Evaluation Metrics  
This paper refers to the evaluation metrics for road damage detection in CRDDC'2022, using the F1 score and mean average precision (_mAP_) to assess the performance of the trained model. The F1 score is the harmonic mean of precision and recall values; maximizing the F1 score ensures reasonably high precision and recall [31]. Precision is the ratio of true positives to all predicted positives, while recall is the ratio of true positives to all actual positives. Both precision and recall are based on the Intersection over Union (IoU) evaluation, described as the overlap area between the predicted bounding box and the true bounding box divided by their union area. It measures the degree of overlap between the predicted and true target boxes, as shown in Figure 12. Since precision and recall are contradictory, increasing one usually decreases the other. Therefore, the F1 score is employed as the final evaluation metric to balance both. _mAP_ is the average of the average precision (_AP_) for different classes. This paper uses mAP50 and mAP50-95 metrics for evaluation. mAP50 represents the average precision at the IoU threshold of 0.5, and mAP50-95 is an extension of mAP50, indicating the average precision at different thresholds (from 0.5 to 0.95 with a step size of 0.05). It offers a comprehensive assessment of the model's accuracy in predicting target boxes under different IoU thresholds. The detailed parameters are as follows:  
Figure 11: RDD2022 data statistics: distribution of images and labels based on countries.  
* True Positive (_TP_): An instance of road damage exists in the real situation and the labeling of the instance is correctly predicted, the overlap area between the predicted bounding box and the ground truth bounding box is more than 50%, and the IoU \(>\) 0.5.
* False Positive (_FP_): The model predicts an instance of road damage at the specific position in the image that does not exist in the ground reality of the image. _FP_ includes cases where the predicted labels do not match the actual labels too.
* False Negative (_FN_): When there are instances of road damage in the real situation, but the model cannot predict the correct label or bounding box for the instance.
* Recall: \[Recall=\frac{TP}{TP+FN}.\] (13)
* Precision: \[Precision=\frac{TP}{TP+FP}.\] (14)
* F1 Score: \[F1 Score=2\frac{Precision\cdot Recall}{Precision+Recall}.\] (15)
* _AP_: The precision-recall (PR) curve \(p(r)\) can be plotted based on precision and recall values, and the _AP_ is the average value of the function \(p(r)\) over the domain of definition \(r\in[0,1]\). \[AP=\int_{0}^{1}p(r)dr\] (16)
* _mAP_: Calculate and average the _APs_ for each category. \[mAP=\frac{\sum_{i=1}^{N}AP_{i}}{N}\] (17)
* Params: The total quantity of learnable parameters in the neural network, including model weights and biases, is used to measure the spatial complexity and size of the model.
* Giga-FLOPs (GFLOPs): The quantity of billion floating-point operations performed by the model per second. GFLOPs is used to evaluate the computational complexity and efficiency of the model.