A method and device for processing ultrasonic data from an industrial inspection device. Ultrasonic reflections from a high-resolution phased array are sampled at high-frequency to create a large volume of data. The data are converted to I/Q data and compressed to a manageable size. An external computer can beamform and render an image without restoring the compressed data to raw form. This method may be used for in-line inspection, downhole inspection, or ultrasonic testing.

A method for detecting structures is provided. The method can include receiving inspection image data characterizing a region of interest of an object being inspected. The regions of interest can include one or more structures of the object. The method can also include determining, using a computer vision algorithm, a structure within the region of interest with respect to photometric properties of pixel data in the inspection image data. The structure can be determined using a predictive model trained to determine image filter parameter values for image filters of the computer vision algorithm based on applying optimization techniques using training image data and annotation data. An indication of the structure can be provided, for example for display or storage in memory. Systems and computer-readable mediums implementing the method are also provided.

An image inspection device includes: an image acquisition unit to acquire an inspection target image; a geometric transformation processing unit to estimate a geometric transformation parameter for aligning a position of an inspection target in the inspection target image with a first reference image in which a position of the inspection target is known, and geometrically transform the inspection target image using the estimated geometric transformation parameter, thereby generating an aligned image in which the position of the inspection target is aligned with the first reference image; an image restoration processing unit to restore the aligned image, using an image generation network to receive an input image generated using the inspection target image and infer the aligned image as a correct image; and an abnormality determination unit to determine an abnormality of the inspection target using a difference image between the aligned image and the restored aligned image.

System and methods for matching color and appearance of a target coating are provided herein. The system includes an electronic imaging device configured to receive a target image data of the target coating. The target image data includes target coating features. The system further includes one or more feature extraction algorithms that extracts the target image features from the target image data. The system further includes a machine-learning model that identifies a calculated match sample image from a plurality of sample images utilizing the target image features. The machine-learning model includes pre-specified matching criteria representing the plurality of sample images for identifying the calculated match sample image from the plurality of sample images. The calculated match sample image is utilized for matching color and appearance of the target coating.

A method and apparatus for performing automated supervision and inspection of an assembly process. The method is implemented using a computer system. Sensor data is generated at an assembly site using a sensor system positioned relative to the assembly site. A three-dimensional global map for the assembly site and an assembly being built at the assembly site is generated using the sensor data. A current stage of an assembly process for building an assembly at the assembly site is identified using the three-dimensional global map. A context for the current stage is identified. A quality report for the assembly is generated based on the three-dimensional global map and the context for the current stage.

Continuous, multiple-point surveying or measurement is performed on large areas or objects. The results may be coordinated or combined with 3D localization systems or methods employing GPS, manual theodolites, range finders, laser radars or pseudolites. One disclosed example describes the use of the invention as applied to the problem of routine and repeated inspection of large aircraft, though the system and method are equally applicable to other objects with large surfaces including ships, bridges and large storage structures like tanks, buildings, and roadways.

A method of tracking a container closure used to seal liquid in a container is provided, comprising applying a trackable indicia to a container closure, wherein the trackable indicia is unique to the container closure; recording an associated data set comprising a plurality of parameters related to the liquid in the container; correlating the associated data set with the trackable indicia; applying the container closure to the container to seal the liquid within the container; maintaining a database of the trackable indicia corresponding to the associated data set; and scanning the trackable indicia to retrieve the associated data set from the database. The method of the invention also includes utilization of a container closure having inherent natural imperfections and/or patterns on the top and/or sides of the closure and obtaining scanned images and data thereof which are analyzed and utilized for closure and container identification and likewise serve as scannable trackable indicia.

An image-based acceptance learning device (2) learns a result of determination as to whether a planar object (1) is acceptable or defective based on at least one of a three-dimensional shape or a color on a surface of the planar object (1). The device (2) includes a surface image receiver (3) to receive an inputted two-dimensional data being image data of the surface of the planar object (1), a determination information receiver (4) to receive an inputted determination information indicating the result of determination as to whether the planar object (1) corresponding to the two-dimensional data is acceptable or defective, and a learner (5) to learn, based on the two-dimensional data and the determination information, a relevant area (1R) including the three-dimensional shape or the color on the surface in the two-dimensional data. The relevant area is a basis for the determination information.

Provided is an object detection device which can detect with high accuracy a symmetrical target object represented in an image. The object detection device includes a memory which stores a model pattern representing a plurality of predetermined features in different positions to each other on a target object when the target object is viewed from a predetermined direction, a feature extraction unit which extracts a plurality of the predetermined features from an image in which the target object is represented, and a collation unit which calculates a degree of coincidence representing a degree of matching between the plurality of the predetermined features of the model pattern and a plurality of the predetermined features extracted from a region corresponding to the model pattern in the image while changing at least one of a relative position or angle, or a relative direction and relative size of the model pattern with respect to the image, and which judges that the target object is represented in the region of the image corresponding to the model pattern when the degree of coincidence is equal to or greater than a predetermined threshold, wherein the predetermined features stored in the memory include a feature of interest which can be used for detecting a position in a specific direction of the target object in the image or which can be used for detecting an angle in a rotational direction centered about a predetermined point of the target object in the image, and the collation unit increases the contribution in the calculation of the degree of coincidence when the feature of interest of the model pattern and a predetermined feature in the image match as compared to the case in which a predetermined feature of the model pattern other than the feature of interest and the predetermined feature in the image match to calculate the degree of coincidence.

An analysis device for visualizing an accuracy of a trained determination device includes an acquisition unit acquiring an image pair of a non-defective product image and a defective product image, an extraction unit extracting an image region of a defective part of the defective product, a generation unit generating a plurality of image regions of pseudo-defective parts, a compositing unit synthesizing each of the image regions of the plurality of pseudo-defective parts with the non-defective product image to generate a plurality of composite images having different feature quantities, an unit outputting the plurality of composite images to the determination device and acquiring a label corresponding to each of the plurality of composite images from the determination device, and a display control unit displaying an object indicating the label corresponding to each of the plurality of composite images in an array based on the feature quantities.