Measurements and characterization for extremely harsh environments require accurate approach especially by means of image-based computer vision. Because of harsh conditions, such as high temperature, pollution, turbulences, radioactive exposure, high energy, direct measurements through conventional sensors are not easy even with recent sensing technologies. Live and/or shortest time-delayed sensing, by means of imaging, can come to help to overcome the aforementioned constraints. The paper outilines the use of sensed images for characterizing the effects of high temperatures, at the inlet of a blast furnace, during the discharge of materials using a chute. This latter is subject to wear due to chemico-physical reactions at around 350-450 °C. Given the specific application related to the harsh environment, two algorithms are comparatively proposed and updated for the purposes of the paper; they are based both on computer vision, namely monadic technique and conventional neural network. For the first technique, virtual sensors have been introduced within the image thanks to sinogram and backprojection subtechniques. The results highlight the effects of the environment on the layers of anti-wear compounds applied on the chute, then they permit to understand the chute life-cycle. Quantitative percentage of material detection has been included as well as specific metrics for machine learning expression.

中文翻译：

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极端恶劣环境下的计算机视觉传感图像方法：高炉溜槽磨损特征

在极端恶劣的环境中进行测量和表征需要准确的方法，尤其是通过基于图像的计算机视觉的方法。由于高温，污染，湍流，放射性暴露，高能量等恶劣条件，即使使用最新的传感技术，通过常规传感器进行直接测量也不容易。借助于成像的实时和/或最短的时延感测可以帮助克服上述限制。本文概述了使用感测图像来表征在使用斜槽排出物料期间高炉入口处高温的影响的方法。由于后者在约350-450°C的温度下会发生物理化学反应，因此容易磨损。鉴于与恶劣环境有关的特定应用，为了本文的目的，比较地提出和更新了两种算法；它们都基于计算机视觉，即单子法技术和常规神经网络。对于第一种技术，由于正弦图和反投影子技术，已经在图像中引入了虚拟传感器。结果突出了环境对施加在溜槽上的抗磨化合物层的影响，然后它们使人们能够了解溜槽的生命周期。材料检测的定量百分比以及机器学习表达的特定指标也已包括在内。由于正弦图和反投影子技术，已经在图像中引入了虚拟传感器。结果突出了环境对施加在溜槽上的抗磨化合物层的影响，然后它们使人们能够了解溜槽的生命周期。材料检测的定量百分比以及机器学习表达的特定指标也已包括在内。由于正弦图和反投影子技术，已经在图像中引入了虚拟传感器。结果突出了环境对施加在溜槽上的抗磨化合物层的影响，然后它们使人们能够了解溜槽的生命周期。材料检测的定量百分比以及机器学习表达的特定指标也已包括在内。