## 3 Implementation Details and Experimental Results
### Comparison of Crack Measurement Results  
The final improved model is compared with several other mainstream target detection models, SSD [38], Faster R-CNN [39], and RetinaNet [40], under the same experimental conditions to verify the detection advantages of this paper's model over the others in pavement crack recognition. SSD uses a VGG16 backbone network, and Faster R-CNN and RetinaNet use a ResNet50 backbone network. Figure 11 displays the detection performance of each model for pavement cracks. The model developed in this study demonstrates the highest detection rate, accurately delineating the entire crack with numerous small bounding boxes. This indicates the significant efficacy of the targeted improvement strategy for long cracks in pavement, as proposed in this paper. Conversely, the SSD model fails to identify some targets and inaccurately positions another defective target. The Faster R-CNN model incorrectly identifies expansion joints as cracks, lacking the precision to distinguish between actual cracks and similar background features effectively. Similarly, the RetinaNet model fails to localize defects accurately, with its detection bounding boxes not enclosing the crack targets adequately. The methodology introduced in this research demonstrates a superior equilibrium between accuracy, computational efficiency, and model compactness, as depicted in Table 5.  
Figure 10: Comparison of actual detection effects between improved model and baseline model. It achieves a mean accuracy (mA) of 86.32% and processes up to 152.7 frames per second (FPS) while maintaining a minimal model size of 15.3 MB, offering near-optimal accuracy alongside greater processing speed and reduced storage requirements compared to existing methods such as SSD, Faster R-CNN, and RetinaNet. The proposed method's efficiency makes it particularly apt for real-time processing applications and deployment on devices with constrained computational capacity, thereby providing an enhanced balance among detection precision, operational velocity, and ease of implementation. ### Comparison of Crack Measurement Results  
The UAV hovered about 4 m in the air to capture images of cracks in concrete pavement. The crack width was calculated following the identification and segmentation of cracks using the aforementioned image processing method. Concurrently, crack widths were manually measured with calipers for comparison. The 25 extracted cracks were selected for a quantitative analysis and numbered #1-#25. The results of the UAV's measurements and the manual measurements are presented in Table 6. The table includes an original image, a segmented image, the measurements of the shooting distances, and the values of the crack widths measured by the UAV and manually. The results reveal that the absolute error remained below 4.14 mm when measuring cracks less than 2 cm wide. However, the binocular camera had resolution limitations, resulting in higher relative errors, with a maximum of 28.89%. Nevertheless, the absolute error remained low. For cracks wider than 2 cm, the absolute error did not exceed 3.53 mm, and the maximum relative error was 9.57%, which remained below the 10% criterion. These findings indicate that the measurements of cracks wider than 2 cm fell within acceptable error limits. These results provide valuable  
Figure 11: Comparison of the actual detection effects of different models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **mAP (\%)** & **FPS (Frames-1)** & **Weight (MB)** \\ \hline SSD & 77.32 & 112.2 & 99.7 \\ Faster R-CNN & 87.25 & 33.7 & 602.4 \\ RetinaNet & 81.94 & 55.1 & 153.2 \\ YOLOv5s & 82.61 & 173.5 & 13.4 \\ Our method & 86.32 & 152.7 & 15.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Test results of different models. empirical support for the utilization of binocular cameras in crack measurement. They demonstrate that under specific conditions, these measurements remain informative despite the presence of relative errors.