The Influence of Uncertainty Contributions on Deep Learning Architectures in Vision-Based Evaluation Systems

Vision-based measurement (VBM) systems are powerful tool to extract quantitative information by acquiring video sequence or static images. When a VBM is applied to the evaluation of nominal properties, such as image characteristics, the term VBM is substituted with vision-based evaluation (VBE) by extending the framework of operation unit to a new concept of evaluation unit (EU) for the image analysis and machine learning phases. To this regard, deep learning (DL) approaches have gained an exponential interest in the research and industrial community, thanks to incredible flexibility toward visual words and the possibility to abandon subjective feature extraction procedures. From such an explosiveness of applications, it emerges the need to conduct studies on the capability of DL strategies to deal with uncertainty contributions, i.e., definitional uncertainty related to the measurand and reference uncertainty that may occur during the calibration process. In order to present a benchmark platform to analyze the effect of the major sources of uncertainties estimated, we use here an atomic force microscopy (AFM) imaging scenario for the evaluation of the effect of nanoparticles exposure on human cells in the laboratory. These studies are nowadays fundamental in toxicity analysis for monitoring the health conditions of workers and for protecting people from atherosclerosis disease. The performance of the proposed VBE-DL system to recognize cell alterations from the AFM images is related to three different sources of uncertainty and a critical analysis of the results achieved is provided.