## 4 Discussion and Conclusions  
This study analyzed the impact of droplet contamination on camera sensor surfaces in terms of both image quality and object detection performance. Image quality degradation caused by droplet contamination was measured using the MTF metric. The results showed that image quality, including sharpness and contrast, deteriorated as droplet volume increased. Specifically, as the droplet volume increased from 1 \(\upmu\)L to 10 \(\upmu\)L, the MTF50 tended to decrease by approximately 10% to 80%. On hydrophobic surfaces, MTF50 loss was relatively small, reaching up to 20%. In contrast, on the hydrophilic surfaces, the MTF50 loss was significantly higher, averaging over 60%. Contaminants on camera lens surfaces degrade captured image quality, making it essential to maintain high image clarity in environments that heavily depend on visual data for decision making, such as intelligent surveillance and autonomous driving systems. To evaluate the effect on object detection performance, we performed a performance degradation analysis using various object detection algorithms such as YOLOv8, DetectoRS, RTMDet, and DINO. The evaluation utilized the mAP metric to determine the impact of droplet contamination. As contamination increased, detection accuracy dropped significantly across these models. While XL-sized objects remained relatively unaffected, smaller objects (such as XS and S) experienced a dramatic decrease in the mAP, particularly when droplet volume exceeded 3 \(\upmu\)L. For instance, the mAP for XS-sized objects dropped from 0.74 to 0.12 under significant contamination, which implies a severe reduction in detection reliability. A comparative evaluation of hydrophobic and hydrophilic surfaces further illustrated the importance of surface properties in mitigating image quality degradation. On hydrophobic surfaces, the droplets tended to maintain a higher contact angle, which helped in preserving visual information by reducing the spread of droplets. On the hydrophilic surface, droplets spread more widely, and mAPloss reaches up to 90%, whereas, on the hydrophobic surface, mAPloss is less than 60%. However, it should also be noted that hydrophobic coatings are not a complete solution. Droplets on hydrophobic surfaces can still scatter light, altering image color properties and complicating automated visual processing. In conclusion, this study experimentally evaluated and analyzed the impact of camera lens surface contamination on image quality and object detection performance using quantitative metrics such as the MTF and mAP. Unlike traditional simulation-based approaches, this research assessed the effects of contamination by applying real-world contaminants to a camera lens and analyzing the resulting image data, providing more reliable and practical results. Furthermore, the evaluation results and methodologies proposed in this study lay a foundation for advancements in lens cleaning technologies for vision-based systems. By quantifying the extent to which contamination affects image quality and object detection performance, this research can help determine the optimal timing for activating sensor cleaning mechanisms and ensure the stable operation of camera systems. However, this study focused primarily on droplet contamination and did not address other contaminants such as dust, insects, or mud. Unlike droplets, contaminants like dust and mud, which are opaque in nature, could cause a more severe obstruction when adhering to the camera sensor surface, potentially leading to a greater decline in object detection performance. Additionally, since our study found that performance degradation due to droplet contamination tended to be proportional to the contaminated area, a similar trend may be observed with opaque contaminants like dust and mud. Future research should evaluate performance degradation in real-world scenarios where multiple contaminants coexist.