An information processing apparatus includes at least one processor configured to execute, composition processing of combining a first image and another image, and image processing on a second image generated through the composition processing, using metadata of the first image and metadata of the second image.

A smart, human-centered technique that uses artificial intelligence and mixed reality to accelerate essential tasks of the inspectors such as defect measurement, condition assessment and data processing. For example, a bridge inspector can analyze some remote cracks located on a concrete pier, estimate their dimensional properties and perform condition assessment in real-time. The inspector can intervene in any step of the analysis/assessment and correct the operations of the artificial intelligence. Thereby, the inspector and the artificial intelligence will collaborate/communicate for improved visual inspection. This collective intelligence framework can be integrated in a mixed reality supported see-through headset or a hand-held device with the availability of sufficient hardware and sensors. Consequently, the methods reduce the inspection time and associated labor costs while ensuring reliable and objective infrastructure evaluation. Such methods offer contributions to infrastructure inspection, maintenance, management practice, and safety for the inspection personnel.

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.

A method is provided. The method includes one or more of receiving, by an image processing device, one or more images from an image capture device. The one or more images are each associated with metadata that includes a common direction. For each of the one or more images, the method further includes adding one or more pairs of parallel lines, converting each of the one or more pairs of parallel lines into intersection coordinates with 2D drawing elements, and calculating construction progress from the intersection coordinates. The 2D drawing includes a 2D floor plan or a 2D elevation plan, and each pair of parallel lines designates one of the start or end of construction during a current period of time.

Provided are a damage diagram creation method, a damage diagram creation device, a damage diagram creation system, and a recording medium capable of detecting damage with high accuracy based on a plurality of images acquired by subjecting a subject to split imaging. In a damage diagram creation method, damage of a subject is detected from each image (each image in a state of being not composed) constituting a plurality of images (a plurality of images acquired by subjecting the subject to split imaging), and thus, damage detection performance is not deteriorated due to deterioration of image quality in an overlapping area. Therefore, it is possible to detect damage with high accuracy based on a plurality of images acquired by subjecting the subject to split imaging. Detection results for the respective images can be composed using a composition parameter calculated based on correspondence points between the images.

The measurement system 100 includes: a measurement apparatus 20 that measures vibrations of an object 40; an imaging apparatus 30 that is located so as to capture an image of the measurement apparatus 20; and a correction processing apparatus 10. the correction processing apparatus 10 includes: a displacement calculation unit 11 that calculates a displacement of the measurement apparatus 20 based on time-series images of the measurement apparatus 20 output from the imaging apparatus 30; a movement amount calculation unit 12 that calculates an amount of movement of the measurement apparatus 20 relative to the imaging apparatus 30, based on the displacement; and a correction processing unit 13 that corrects vibrations of the object measured by the measurement apparatus 20, using the calculated amount of movement of the measurement apparatus 20.

Tunnel defect detecting method and system using unmanned aerial vehicle (UAV) are provided, and the UAV is equipped with a light-emitting diode (LED) module, a camera, a laser radar, an ultrasonic distance meter and an inertial measurement unit (IMU). The method includes: collecting images in a tunnel based on the LED module and the camera to obtain a training image set; training by using the training image set to obtain a defect detecting model, collecting real-time tunnel images, detecting suspected defects to the real-time tunnel images by the defect detecting model, obtaining pose information of the UAV based on the camera, the laser radar, the ultrasonic distance meter and the IMU to control the UAV to hover. The method can realize accurate pose estimation and defect detection in the tunnel with no GPS signals and highly symmetrical inside.

An information processing apparatus selects, as a reference defect, at least one defect from among first defects associated with a first image and selects, as a correction target defect, at least one defect from among second defects associated with a second image captured at a time different from an image capturing time of the first image. Additionally, the information processing apparatus generates a correction candidate by modifying the correction target defect, acquires a matching level representing a matching relationship between the reference defect and the correction candidate, and generates a corrected defect by correcting the correction target defect based on the matching level. Then, the information processing apparatus acquires a progress level representing a change in defect from the reference defect based on a comparison between the reference defect and the corrected defect.

A displacement component detection apparatus 10 is provided with: a displacement distribution calculation unit 11 configured to calculate, from time-series images of a measurement target region of an object 30 output from an image capturing device 20 configured to capture the images of the measurement target region, a displacement distribution in a region that corresponds to the measurement target region in the images; a movement amount calculation unit 12 configured to calculate, based on the displacement distribution and image capturing information, a movement amount in the surface direction of the measurement target region and a movement amount in the normal direction of the measurement target region; and a surface displacement calculation unit 13 configured to calculate, from the displacement distribution, a surface displacement component in the measurement target region, using the movement amount in the surface direction of the measurement target region and the movement amount in the normal direction of the measurement target region.

A computer-implemented method for analyzing an image to detect an article of interest (AOI) comprises processing the image using a machine learning algorithm configured to detect the AOI and comprising a convolutional neural network (CNN); and displaying the image with location of the AOI being indicated if determined to be present. The CNN comprises an input module configured to receive the image and comprising at least one convolutional layer, batch normalization and a nonlinear activation function; an encoder thereafter and configured to extract features indicative of a present AOI to form a feature map; a decoder thereafter and configured to discard features from the feature map that are not associated with the present AOI and to revert the feature map to a size matching an initial image size; and a concatenation module configured to link outputs of the input module, the encoder and the decoder for subsequent segmentation.