Abstract: This disclosure relates to method and system for inspection of rotational components based on video frames of the rotational components in motion. Based on the property of projective single axis motion, motion trajectories of defects on the rotational components are modeled in a conic pattern. The defects are clustered according to their spatial information around a rotation axis of the rotational components to ascertain distinct defects. Undetected defects may be recovered by rotating their previous locations along their trajectory conics by a predetermined rotation angle. Based on the distinct defects, including the recovered defect, a modified video frame which includes an identification of the distinct defects, including the recovered defect, may be generated.

Abstract: This disclosure relates to method and system for inspection of rotational components based on video frames having different camera views of defects on rotational components. Using optical flow to ascertain motion vectors of pixels of the video frames, the video frames are partitioned based on motion vectors. The partitioned regions are paired with corresponding regions in a subsequent video frame, and their features are matched. For video frames having at least camera motion, a transformation matrix is ascertained which is applied to map defect trajectories of each camera view to a subsequent camera view.

Abstract: This invention relates to a method for training a neural network, comprising detecting a hole in each training image of a plurality of training images; transforming each training image into a transformed image, to suppress non-crack information in the training image; and training a neural network using the transformed images, to detect cracks in images (i.e. in objects in images).

Abstract: This invention relates to a method for training a neural network, comprising detecting a hole in each training image of a plurality of training images; transforming each training image into a transformed image, to suppress non-crack information in the training image; and training a neural network using the transformed images, to detect cracks in images (i.e. in objects in images).