A method for image analysis, the method comprising: obtaining a series of images of a rotating target object (210, 310) through multiple revolutions of the target object (210, 310); grouping the images into multiple, different sets (502, 504) of images, the images in each of the different sets (502, 504) depicting a respective common portion of the target object (210, 310), at least some of the images in each set obtained during a different revolution of the target object (210, 310); examining the images in a selected set of the multiple sets (502, 504) of images using an artificial neural network for automated object-of-interest recognition by the artificial neural network; and performing one or more times: responsive to the artificial neural network not recognizing an object-of-interest in the selected set of the multiple sets (502, 504) of images, selecting a different set of the multiple sets (502, 504) of images as the selected set, and examining the images in the newly selected set using the artificial neural network for automated object-of-interest recognition by the artificial neural network; or responsive to the artificial neural network recognizing an object-of-interest in the selected set of the multiple sets (502, 504) of images, performing a remedial or responsive action in respect of the recognized an object-of-interest; wherein the artificial neural network is a recurrent neural network; wherein the target object (210, 310) is a turbine assembly (210, 310) including multiple airfoils (304, 304A-304F), the images in each set depicting a respective common airfoil of the turbine assembly (210, 310); and wherein the artificial neural network is configured to recognize at least one of spalling or cracks of an airfoil (304, 304A-304F) or an area on the airfoil (304, 304A-304F) in which a color change is identified as objects-of-interest. obtaining a series of images of a rotating target object (210, 310) through multiple revolutions of the target object (210, 310); grouping the images into multiple, different sets (502, 504) of images, the images in each of the different sets (502, 504) depicting a respective common portion of the target object (210, 310), at least some of the images in each set obtained during a different revolution of the target object (210, 310); examining the images in a selected set of the multiple sets (502, 504) of images using an artificial neural network for automated object-of-interest recognition by the artificial neural network; and performing one or more times: responsive to the artificial neural network not recognizing an object-of-interest in the selected set of the multiple sets (502, 504) of images, selecting a different set of the multiple sets (502, 504) of images as the selected set, and examining the images in the newly selected set using the artificial neural network for automated object-of-interest recognition by the artificial neural network; or responsive to the artificial neural network recognizing an object-of-interest in the selected set of the multiple sets (502, 504) of images, performing a remedial or responsive action in respect of the recognized an object-of-interest; wherein the artificial neural network is a recurrent neural network; wherein the target object (210, 310) is a turbine assembly (210, 310) including multiple airfoils (304, 304A-304F), the images in each set depicting a respective common airfoil of the turbine assembly (210, 310); and wherein the artificial neural network is configured to recognize at least one of spalling or cracks of an airfoil (304, 304A-304F) or an area on the airfoil (304, 304A-304F) in which a color change is identified as objects-of-interest. responsive to the artificial neural network not recognizing an object-of-interest in the selected set of the multiple sets (502, 504) of images, selecting a different set of the multiple sets (502, 504) of images as the selected set, and examining the images in the newly selected set using the artificial neural network for automated object-of-interest recognition by the artificial neural network; or responsive to the artificial neural network recognizing an object-of-interest in the selected set of the multiple sets (502, 504) of images, performing a remedial or responsive action in respect of the recognized an object-of-interest; wherein the artificial neural network is a recurrent neural network; wherein the target object (210, 310) is a turbine assembly (210, 310) including multiple airfoils (304, 304A-304F), the images in each set depicting a respective common airfoil of the turbine assembly (210, 310); and wherein the artificial neural network is configured to recognize at least one of spalling or cracks of an airfoil (304, 304A-304F) or an area on the airfoil (304, 304A-304F) in which a color change is identified as objects-of-interest.