Thermal Images with different amount of contaminats on the lens

The paper assesses the effectiveness of sharpness metrics for monitoring the cleanliness of in-line thermographic systems and enabling self-diagnosis, in order to prevent degradation of metrologic performance and increase of measurement uncertainty. When optical measurement systems are installed in harsh industrial environments, external contaminants may compromise their operation conditions. Various types of dust may settle on the lens or protective window, deteriorating the quality of the generated signal. Particularly, when the observed object is at very high temperatures, the thermal image produced by a thermographic inspection system loses sharpness, making it difficult to identify object edges and decreasing the output data quality. Monitoring image sharpness over time helps maintain system cleanliness and ensure the quality of the generated data. The effectiveness of blur metrics, commonly employed to assess proper focus or the quality of images of moving objects, is evaluated to understand if image quality has deteriorated due to external contaminants. The metrics judged best were applied to data from a real industrial case to confirm their efficacy.

该论文评估了锐度指标在监控在线热成像系统清洁度、实现自我诊断方面的有效性，旨在预防计量性能的退化及测量不确定性的增加。当光学测量系统被安装在恶劣的工业环境中时，外部污染物可能损害其运行条件。各种类型的尘埃可能沉积在镜头或保护窗口上，降低生成信号的质素。特别是，当观测对象处于极高温度时，热成像检测系统产生的热图像会失去锐度，导致物体边缘难以识别，进而降低输出数据的质量。通过对图像锐度随时间的变化进行监控，有助于维持系统清洁并确保生成数据的品质。对模糊度指标的有效性进行了评估，这些指标通常被用于评估适当的焦距或移动物体图像的质量，以了解图像质量是否因外部污染物的存在而下降。经过评估，被认为最有效的指标被应用于实际工业案例的数据中，以验证其有效性。