Effect of Droplet Contamination on Camera Lens Surfaces: Degradation of Image Quality and Object Detection Performance

Recently, camera sensors have been widely used in a variety of applications, including advanced driver assistance systems (ADASs), surveillance systems, and unmanned aerial vehicles (UAVs). These sensors are often integrated with intelligent algorithms to automatically analyze information and perform specific functions. However, during rainy weather, droplets on the camera lens surface can obstruct the view, leading to degraded image quality and reduced algorithm performance. This paper quantitatively evaluates the effect of droplet contamination on image quality and object detection performance. Image quality degradation was analyzed using the modulation transfer function (MTF), with droplet volume and number as variables. Results show that the MTF50 decreased by up to 80% when the droplet volume reached 10 μL. To assess the effect on object detection, performance changes were evaluated across different detection algorithms. The findings reveal that droplet contamination can reduce the detection performance of small objects by up to 90%, as measured by the mean average precision (mAP) metric. Furthermore, degradation was more severe on hydrophilic surfaces compared to hydrophobic ones. This study demonstrates that droplet characteristics such as volume, number, and shape significantly influence both image quality and object detection performance. It provides critical insights into selecting appropriate camera lens materials by comparing hydrophilic and hydrophobic surfaces while also highlighting the susceptibility of intelligent algorithms to environmental factors and underscoring the importance of effective cleaning techniques.