## 1 Introduction  
The camera sensor, the 'eye' of many systems, is becoming increasingly important as technology advances. Camera sensors capture the visible spectrum of light reflected by objects and convert it into digital images, providing information analogous to human vision. These sensors are available in a variety of configurations, including different lens sizes and pixel resolutions to meet the needs of diverse applications [1, 2, 3, 4, 5]. Recently, camera sensors and artificial intelligence (AI)-based image analysis algorithms have been integrated to perform automated functions in various applications such as AI surveillance in smart cities [6, 7], advanced driver assistance systems (ADASs) [8, 9], unmanned aerial vehicle (UAVs) [10, 11], smart agriculture [12], and intelligence robotics [13]. For example, in autonomous driving systems like ADASs, AI-based algorithms analyze data collected from a vehicle's front camera sensors to recognize road lanes, traffic signals, pedestrians, and vehicle movements in real-time. This enables important functions such as forward collision avoidance and lane-keeping assistance to work smoothly [14]. Similarly, in AI surveillance systems, the combination of deep learning algorithms and camera sensors enables real-time object detection, behavior recognition, crowd analysis, and more to quickly detect potential threats. This is crucial in complex environments where rapid response is required [15]. However, these cameras are often operated in outdoor environments, where their lens surfaces are easily contaminated by environmental elements such as raindrops, dust, mud, and insects [16; 17; 18]. If the lens surface is contaminated by these elements, the performance of the camera can be significantly reduced, which has a major impact on the reliability and stability of the entire system. Figure 1 illustrates the distortion of images when a camera lens surface is contaminated by water droplets, one of the most common environmental contaminants. A clean camera sensor provides clear information about the people and objects, as shown in Figure 1a. In contrast, when the camera sensor is contaminated by water droplets, the image quality is noticeably degraded, as shown in Figure 1b. Image quality degradation due to the contaminants can also affect the performance of AI-based image analysis algorithms. For example, contaminants on the lens surface of an autonomous camera can cause the ADAS to miss or falsely detect objects, leading to traffic accidents [19]. Similarly, contaminants on the lens surface of surveillance cameras can produce low-quality images and videos, making it harder to collect evidence and delaying incident responses. For this reason, many studies have recently been conducted on robustness enhancement algorithms to maintain object detection performance under external noise [20; 21]. In  
Figure 1: Image quality degradation by droplet contamination on the camera lens surface. particular, remote sensing applications, where detecting small objects is essential, have leveraged various learning-based approaches such as strong-classification weak-localization [22], adversarial training [23], and pyramid representations [24] to improve robustness. However, these robustness enhancing methods do not fundamentally address performance degradation caused by physical contaminants on the camera lens surface. To address the issue of physical contamination on the lens surface, various decontamination technologies are currently under investigation. For instance, studies have been conducted to remove water droplets from the lens surface using electrowetting [25; 26], acoustic wave [27; 28], thermal technology [29; 30] and restoring distorted images using deep learning-based algorithms [31; 32]. These technologies contribute to preventing system performance degradation by quickly decontaminating the sensor or restoring the image. However, there is a lack of research that has systematically evaluated the effects of contaminants on camera sensors.