## 3 Object Detection Performance Evaluation Results
### Object Detection Performance Measurement Setup and Method  
To evaluate the impact of camera sensor contamination on object detection performance using AI algorithms under various external conditions, a simulated environment was constructed as shown in Figure 9a. A UHD 3840 \(\times\) 2160 resolution monitor (86 UM8070 PUA, LG Electronics, Seoul, Republic of Korea) was used to display images of diverse environments such as roads, people, and vehicles, representing different vision scenarios where camera systems are utilized. The camera was positioned 100 cm away from the monitor. To replicate backlighting conditions often encountered outdoors, such as car headlights and streetlights, two halogen lamps were placed on either side of the camera sensor. In outdoor environments, lighting conditions exhibit high complexity and variability due to differences in the types, locations, and intensities of light sources. In particular, dynamic light sources such as vehicle headlights and taillights undergo continuous changes in position and brightness, altering the direction and intensity of light on droplets on the lens surface. These variations can influence the optical properties of reflection, transmission, and refraction, potentially leading to fluctuations in object detection performance. In this study, we used two fixed light sources to ensure consistency of the experiments and minimize the impact of lighting variables. The captured images were stored on the PC (HPE ProLiant DL380 Gen11, Hewlett Packard Enterprise) and utilized to evaluate the performance of the object detection algorithm.  
Figure 9: **(a)** Image capture setup for acquiring the object detection performance evaluation dataset. **(b)** Object detection performance evaluation process: **(b1)** The preparation of droplet-contaminated and clean datasets. **(b2)** Inference of the collected dataset with object detection model. **(b3)** Calculation of the mAP based on the precision–recall curve.  
Figure 9b illustrates the object detection performance evaluation process. First, the prepared images were displayed on the monitor. The monitor was photographed by the camera sensor with the lens cover glass both clean and contaminated by droplets. The droplet contamination method used was the same as described in Section 3. Next, object type and location information were labeled on the images using DarkLabel 2.4 software, with the labeled files serving as the ground truth. Finally, performance was assessed by comparing the labeled files to the captured images.  
To thoroughly evaluate object detection performance, it is important to use algorithms with different architectures. We evaluated the performance using YOLOv8 and RTMDet [43], which are CNN-based one-stage methods that simultaneously predict object locations and classifications. Additionally, we used DetectoRS [44], a two-stage method that performs classification after predicting object locations, and DINO [45], a transformer-based method that analyzes global image information. All object detection models, each based on different algorithms, were trained on the COCO dataset to ensure consistency in training data and reduce variability.