A first imaging unit 71 generates a first image a first image by taking an object to be inspected. A guide display unit 72 determines the object to be inspected from the first image by using a model for determining an object to be inspected from an image, and displays an illustration representing the object to be inspected as a guide. A second imaging unit 73 generates a second image by superimposing on the guide, and taking the object to be inspected with a recognizable marker regardless of color of an appearance of an object to be inspected, attached in a vicinity of a defect. A defect position determination unit 74 determines a position of the defect included in the object to be inspected based on a positional relationship between the illustration and the marker included in the second image. An information collecting unit 75 collects defect information associated with a type of the object to be inspected and the position of the defect.

A system for taking high-resolution photographs from a vehicle-mounted camera, forming orthomosaics from video and/or multiple high-resolution photographs, and using artificial intelligence to detect and classify pavement flaws and defects in the imagery. Detection also includes the ability to capture quantifiable metrics for the defects and/or a region of interest. Three-dimensional imagery is produced from the same images as the orthomosaics. Surface and terrain map products made from the same source images capture additional details such as depth and volume. The highlighted orthomosaics and three-dimensional imagery can then be used as a basis to determine the pavement surface condition and subsequently support maintenance orders and manage pavement repairs. Further, metadata such as latitude, longitude, and altitude geo-location coordinates and sampling time can also be transferred to the output products to create a digital time history and enable analysis for preventative maintenance planning. Alternatively-sourced imagery may also be analyzed.

A system for inspecting features of an airframe, the system including a feature inspection device configured to measure an aspect of a first feature and a tracking subsystem configured to determine a position of the feature inspection device when the feature inspection device measures the aspect of the first feature. The system is configured to determine a position of the first feature on the airframe via the feature inspection device and the tracking subsystem, the determination of the position of the first feature being independent from the measurement of the aspect of the first feature.

The present invention relates to a method for cancer cell separation, and more specifically, relates to a method for cancer cell separation using micro-vibration.

In an embodiment, a method for detecting cracks in road segments is provided. The method includes: receiving raw range data for a first image by a computing device from an imaging system, wherein the first image comprises a plurality of pixels; receiving raw intensity data for the first image by the computing device from an imaging system; fusing the raw range data and raw intensity data to generate fused data for the first image by the computing device; extracting a set of features from the fused data for the first image by the computing device; providing the set of features to a trained neural network by the computing device; and generating a label for each pixel of the plurality of pixels by the trained neural network, wherein a received label for a pixel indicates whether or not the pixel is associated with a crack.

A system for inspecting features of an airframe, the system including a feature inspection device configured to measure an aspect of a first feature and a tracking subsystem configured to determine a position of the feature inspection device when the feature inspection device measures the aspect of the first feature. The system is configured to determine a position of the first feature on the airframe via the feature inspection device and the tracking subsystem, the determination of the position of the first feature being independent from the measurement of the aspect of the first feature.

A method, apparatus, and system for inspecting a fuselage of an aircraft. A rail is positioned within an interior portion of the fuselage of the aircraft such that the rail extends through the interior portion of the fuselage. An inspection device is attached to the rail. The inspection device moves along the rail and the position of the rail enables the inspection device to inspect the interior portion. Video data is sent to a computer system over a wireless communications link between the inspection device and the computer system. The video data is displayed on a display system for the computer system. Inspection operations are performed in the interior portion of the fuselage with the inspection device attached to the rail when a group of commands to perform the inspection operations are received from the computer system over a wireless communications link between the inspection device and the computer system.

The present invention generally relates to assigning and reporting the cleanliness of objects and areas, and particularly relates to utilization of object and area cleanliness states as determined by the opposing processes of cleaning and dirtying detected through various methods to provide an indication of the state of cleanliness potentially utilized to alter the process of cleaning and/or dirtying to reach a desired state of cleanliness. Detection of the cleaning and dirtying operations can be performed automatically through image processing and behavior detection of still images/video indicating the activity taking place in the area of interest and time. The state of cleanliness can then be reported to interested parties as textual reports and/or augmented reality overlays on still images/video.

In an embodiment of the disclosed principles, a method of analyzing a rope to estimate its residual break strength (RBS) is provided. The method entails training a multi-layered neural network to recognize rope damage and estimate an RBS for a rope under study by collecting high-resolution visual data of a plurality of sample ropes, extracting visual features of damaged areas of each rope, resolving each visual feature into a damage type and clustering damage type exemplars, breaking each sample rope to determine an actual RBS for each rope and classifying the visual data via determined RBS, specific to product type/damage mode. Job and product data are entered for the rope under study, the rope is paid out, and high-resolution visual data is captured while spooling back the rope. The correlated data is provided to the multi-layered neural network to generate an estimate of RBS for the rope under study.

A system for inspecting features of an airframe, the system including a feature inspection device configured to measure an aspect of a first feature and a tracking subsystem configured to determine a position of the feature inspection device when the feature inspection device measures the aspect of the first feature. The system is configured to determine a position of the first feature on the airframe via the feature inspection device and the tracking subsystem, the determination of the position of the first feature being independent from the measurement of the aspect of the first feature.