The present invention proposes a method for identifying the spatial-temporal distribution of the vehicle loads on a bridge based on the DenseNet. The method includes five steps: firstly, mounting a plurality of cameras in different positions of a bridge, acquiring images of the bridge from different directions, and outputting video images with time tags; secondly, acquiring multichannel characteristics of vehicles on the bridge by using DenseNet, including color characteristics, shape characteristics and position characteristics; thirdly, analyzing the data and characteristics of the vehicles from different cameras at a same moment to obtain vehicle distribution on the bridge at any time; fourthly, continuously monitoring the vehicle distribution in a time period to obtain a vehicle load situation on any section of the bridge; and finally, integrating the time and space distribution of the vehicles to obtain spatial-temporal distribution of the bridge.

In order to efficiently provide an image suitable for detecting a defect in a structure, an image processing apparatus inputs an image and a parameter for geometrically correcting the image, calculates a resolution of a corrected image obtained in a case of geometrically correcting the image using the parameter, and displays resolution information relating to the calculated resolution in association with the image.

A system (1) for examining an object (2) for errors comprises a monitoring device (3), a processing module (5), a capturing device (4) and a recognition module (6). The monitoring device (3) is designed to monitor at least one parameter. A specified range of the parameter defines a context within which a result of a recognition of at least parts of the object (2) is expected. The processing module (5) is designed to prove whether the monitored parameter is within the specified range and in this case to trigger the capturing device (4) which is designed to capture input data associated with the object (2). The recognition module (6) is pre-trained for recognizing the object (2) and to perform the recognition based on the input data. The recognition module (6) is designed to detect an error if a result of the recognition is not corresponding to the expected result.

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 method for determining a position of an image capture device (ICD) within a building site, the module comprising: registering an image captured in the building site by the ICD; registering an initial ICD position of the ICD at the time the image was captured; generating a plurality of proposed ICD positions based on the initial ICD position; and selecting an optimized ICD position from the plurality of proposed ICD positions. Optionally, the optimized ICD position is selected responsive to a model of the building site that comprises sufficient information to generate a three dimensional (3D) representation of the building site.

Described herein are systems and methods for imaging the top surface of a fuel pellet to observe the formation of radial cracks employing resistive heating to volumetrically heat the fuel pellet, but instead of passing the current axially through the pellet, electrodes were placed on the sides of a single pellet to pass the current transversely across the pellet allowing for an unobstructed view of the top surface of the pellet.

A state determination apparatus 100 determines the state of a structure 200. The state determination apparatus 100 includes a measurement unit 10 configured to measure a deflection amount and a surface displacement amount in each of a plurality of target regions that are preset on the structure 200, a feature value calculation unit 20 configured to calculate, for the respective target regions, feature values each indicating a relationship between the deflection amount and the surface displacement amount, using the measured deflection amount and surface displacement amount, a spatial distribution calculation unit 30 configured to calculate a spatial distribution of the feature values using the feature values calculated for each of the target regions, and a degradation state determination unit 40 configured to determine a degradation state of the structure 200 based on the spatial distribution of the feature value.

The present invention relates to an aircraft-utilizing deterioration diagnostic system and provides a technique capable of improving diagnostic efficiency and accuracy when comparing the previous and current aerially-photographed images to diagnose a deteriorated state of a target. The diagnostic system includes: a drone 1 (aircraft) configured to be navigated along a route around a target 5 and having a camera 4 configured to photograph the target 5; and a computer (PC 2 and server 3) configured to control navigation of the aircraft and photographing by the camera 4. The computer is configured to: acquire data from the aircraft including an image group obtained by consecutively photographing the target 5 for each predetermined date and time; based on diagnostic data including a diagnostic image group photographed at a current date and time, reference data including a reference image group of a previous date and time, associate, for the same target 5, images including the same portion as comparison target images; compare the previous image and the current image among the comparison target images to detect a deteriorate portion; and provide a screen converted and visualized for the user such that the deteriorated portion in the image is plotted on the three-dimensional model of the target.

There is provided with an information processing device. A defect detecting unit detects a defect of an object in an input image. An extracting unit extracts a feature amount pertaining to a partial image of the defect from the input image, on the basis of a result of detecting the defect. An attribute determining unit determines an attribute of the defect using the feature amount pertaining to the partial image of the defect.

Systems and methods for detecting cracks in a surface by analyzing a video, including an full-HD video, of the surface. The video contains successive frames, wherein individual frames of overlapping consecutive pairs of the successive frames have overlapping areas and a crack that appears in a first individual frame of a consecutive pair of the successive frames also appears in at least a second individual frame of the consecutive pair. A fully convolutional network (FCN) architecture implemented on a processing device is then used to analyze at least some of the individual frames of the video to generate crack score maps for the individual frames, and a parametric data fusion scheme implemented on a processing device is used to fuse crack scores of the crack score maps of the individual frames to identify cracks in the individual frames.