## 3 Object Detection Performance Evaluation Results
### Object Detection Performance Degradation Effects of Single Droplet and Object Size  
Generally, images contain objects of various sizes, depending on the size of the object and the distance between the object and the camera. When evaluating the performance of the object detection algorithms, it is important to analyze the relationship between the size of the object in the image and the size of the contaminant. In this section, we analyze the effect of object size and single droplet volume on the performance of object detection algorithms.  
The images displayed on the monitor for evaluation were constructed by taking pictures of objects at different distances between the camera and the object from 5 m to 50 m in an outdoor environment. Each image included five individuals, and the object sizes, which varied depending on the distance, were categorized as XL, L, M, S, and XS. After applying droplets of various volumes to the cover glass, the evaluation dataset was generated by capturing images containing objects of different sizes. Table 3 presents the object sizes and the corresponding number of images in the evaluation dataset.  
Figure 10 shows the evaluation datasets and performance evaluation results (mAP) when two types of cover glass were contaminated by a single droplet. The evaluation dataset contains the person class only, and the YOLOv8x model was utilized as the object detection model. The YOLO is one of the most well-known algorithms in object detection, especially the YOLOv8 algorithm released in 2023, which is known for its high accuracy. The single droplets on both types of surfaces hindered object identification, and the droplet over 3 \(\mu\)L on hydrophilic surfaces completely obscured objects.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Object Size**} \\ \cline{2-6}  & **XL** & **L** & **M** & **S** & **XS** \\ \hline Average object size [pixel\({}^{2}\)] & 40,009 & 11,508 & 2247.3 & 773.86 & 564.55 \\ Number of images in the dataset & 200 & 400 & 800 & 400 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Size of object and number of images in the evaluation dataset for assessing single droplet contamination effects.  
Figure 11a shows the mAP of the YOLOv8 for the person class. When the cover glass was clean, the mAP was 0.989 for XL-size objects and 0.74 for XS-size objects. However, when the glass surface was contaminated by a single droplet, the mAP decreased on both hydrophilic and hydrophobic surfaces. The mAP\({}_{\text{loss}}\) calculated based on Equation (7) is shown in Figure 11b. For the same object size, the mAP decreased significantly as the droplet volume increased. Notably, on hydrophilic surfaces, droplets larger than 3 \(\mu\)L reduced the mAP to 0. Furthermore, the degradation in mAP was more pronounced for smaller object sizes, such as S and XS, compared to larger sizes like XL or L. These findings highlight that smaller objects are more heavily impacted by droplet contamination, which can lead to critical errors in systems that rely on detecting small objects.  
Figure 10: Images for assessing the effects of single droplet contamination, with droplet volumes (1, 3, 5, and 10 \(\mu\)L) and object sizes (XL, L, M, S, and XS) as variables on (**a**) hydrophobic and (**b**) hydrophilic cover glass.