# Improved YOLOv5-Based Real-Time Road pavement Damage Detection in Road Infrastructure Management
## 4 Result Analysis
### Comparison of Different YOLO-Based Object Detectors  
All state-of-the-art large, medium, and small-sized versions of the Yolo model's results are given in Table 5. The YOLOv8l, YOLOv5l, YOLOv8m, and YOLOv5m represent all the large and medium models that we trained with the RDD 2022 dataset where we obtained the best mAP and F1 score but with a large number of parameters and low FPS. The YOLOv8l attains the highest mAP of 71.50% with 43.7 million parameters among these models. The YOLOv8m achieved 70.79% mAP and 68.63% F1 score with 25.80 million parameters. With YOLOv5l and YOLOv5m, we obtained 70.90% and 69.23% mAP, corresponding to 46.50 and 21.2 million parameters. These large and medium-sized models' parameter counts and higher GFlops make the model heavyweight and unsuitable for real-time damage detection. All the small-sized models were experimented with, and they are suitable for real-time damage detection and more lightweight to integrate into mobile  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Precision** & **Recall** & **F1 Score** & **[EMAIL_ADDRESS]** & **No. of** & \multirow{2}{*}{**FPS**} \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **Params. (M)** & \\ \hline YOLOv5s-Baseline & 66.70 & 63.80 & 65.22 & 65.91 & **7.06** & **68** \\ YOLOv5s-Baseline + ECA & 66.83 & 63.93 & 65.33 & 65.99 & 7.18 & 59 \\ YOLOv5s-Baseline + ECA & 67.94 & 62.96 & 65.35 & 66.26 & 7.18 & 59 \\ YOLOv5s-Baseline + ECA & 68.91 & 62.64 & 65.62 & 66.57 & 7.07 & 66 \\ YOLOv5s-Baseline + ECA & & & & & & \\ + Focal + K-means++ + label & 67.94 & 63.03 & 65.39 & 66.74 & 7.07 & 66 \\ smoothing & & & & & & \\ YOLOv5s-Baseline + ECA & & & & & & \\ + Focal + K-means++ + label & **70.14** & **63.24** & **66.51** & **67.81** & 8.11 & 58 \\ smoothing + Newly Added & & & & & & \\ Detection layer & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study on improved YOLOv5s.  
Figure 8: Demonstration of tiny object detection using our improved YOLOv5s: (**a**) YOLOv5s; (**b**) our improved YOLOv5s.  
devices. To assess this, we trained the dataset with YOLOv7-tiny, YOLOv5s, YOLOv8s, and our improved YOLOv5s.  
Figure 10: Training vs. validation curve of our improved YOLOv5s: (**a**) class_loss; (**b**) box_loss; (**c**) object_loss.  
Figure 9: Demonstration of our improved YOLOv5s model detection results: (**a**) input image; (**b**) ground truth; (**c**) detection output.  
The YOLOv5s attains 65.91% mAP with 7.01 million parameters. YOLOv7-tiny consists of 6.27 million parameters, and there is a decrease of 0.24% in mAP from the YOLOv5s. Yolov8s consists of 11.2 million parameters, which is 4.2 million more than the v5s, and contains larger GFlops than YOLOv5s, and it achieved 67.70% mAP. However, we obtained a 1.9% improvement from Yolov5 in terms of mAP and 1.29% in terms of F1 while increasing by only 1.1 million parameters. Moreover, a 0.11% improvement in mAP and 0.05% in F1 score was achieved from YOLOv8s while having 3 million fewer parameters and 12 gigabytes less GFlops.