Provided are a learning data collection apparatus, a learning data collection method, and a program for collecting learning data to be used for efficient retraining. A learning data collection apparatus (10) includes an inspection image acquisition unit (11) that acquires an inspection image, a region detection result acquisition unit (damage detection result acquisition unit (13)) that acquires a region detection result the region detection result indicating a region detected by a region detector that is trained, a correction history acquisition unit (15) that acquires a correction history of the region detection result, a calculation unit (17) that calculates correction quantification information obtained by quantifying the correction history, a database that stores the inspection image, the region detection result, and the correction history in association with each other, an image extraction condition setting unit (19) that sets a threshold value of the correction quantification information as an extraction condition, the extraction condition being a condition for extracting the inspection image to be used for retraining from the database, and a first learning data extraction unit (21) that extracts, as learning data for retraining the region detector, the inspection image satisfying the extraction condition and the region detection result and the correction history that are associated with the inspection image.

The present disclosure relates generally to evaluating the surfaces of a building. The present disclosure relates more particularly to a method of characterizing a surface texture of a building surface. The method includes illuminating a first area of the building surface from a single direction and capturing an image of the first area using a camera while the first area is illuminated. The first image includes a first group of digital pixel values. The method further includes calculating a first set of values that characterize a first surface texture of the first area based on a first group of digital pixel values of the image, and comparing the first set of values to a second set of values that characterize a second surface texture, so as to produce a comparator value.

An image processing apparatus includes detecting means for detecting a first detected region and a second detected region from an input image, on the basis of a first detection criterion and a second detection criterion, respectively; image setting means for setting, as a target image subjected to correction, an image including the first detected region, and setting, as a reference image that is referred to in the correction, an image including the second detected region; accepting means for accepting, from a user, designation of a region in the target image and a correction instruction for the designated region; correction region setting means for identifying, in the reference image, a region corresponding to the designated region, and for setting a to-be-corrected region on the basis of the identified region and the second detected region; and correcting means for correcting the first detected region in the target image on the basis of the to-be-corrected region set in the reference image.

Disclosed is a comprehensive inspection vehicle for a subway tunnel, including a positioning system, an acquisition system and a flatcar. The flatcar runs on a railway of the subway tunnel. The positioning system and the acquisition system are arranged on the flatcar. The positioning system includes a laser ranging module and an inertial navigation module. The comprehensive inspection vehicle further includes an independent power supply system and a central control system arranged on the flatcar. The independent power supply system supplies power for the acquisition system, the positioning system and the central control system. The central control system includes an acquisition industrial computer and a positioning industrial computer which are respectively connected to the acquisition system and the positioning system.

Systems and methods suitable for capable of autonomous crack detection in surfaces by analyzing video of the surface. The systems and methods include the capability to produce a video of the surfaces, the capability to analyze individual frames of the video to obtain surface texture feature data for areas of the surfaces depicted in each of the individual frames, the capability to analyze the surface texture feature data to detect surface texture features in the areas of the surfaces depicted in each of the individual frames, the capability of tracking the motion of the detected surface texture features in the individual frames to produce tracking data, and the capability of using the tracking data to filter non-crack surface texture features from the detected surface texture features in the individual frames.

An evaluation value of a sense of temperature that is closer to human perception is estimated. An image feature amount extraction unit (11) extracts an image feature amount from an input image. A temperature sense estimation unit (12) estimates a temperature sense score from the image feature amount with use of a temperature sense estimation model in which a correlation between the image feature amount and the temperature sense score has been learned in advance. The temperature sense score may be weighted with use of a material weight previously set with respect to the material information corresponding to the input image. As the image feature amount, representative values of coordinates a*, b* in a Lab three-dimensional space or a color histogram in which the Lab three-dimensional space is divided into the predetermined number may be used.

A repair length determination method that can accurately calculate a repair region (repair length) in which repair is to be performed in a structure and an apparatus are provided. The repair length determination method has: an image acquiring step of acquiring an image obtained by imaging a repair target region; a detection step of detecting a crack from the image; a display step of displaying a crack; a pointing step of pointing a plurality of points including two points at both ends of the crack; an interpolation curve creating step of creating an interpolation curve by using the pointed points; and a repair length determining step of measuring a length of the interpolation curve and determining a repair length. The apparatus is used for the determination method.

A TBM-mounted virtual reconstruction system and method for a tunnel surrounding rock structure, the method including: obtaining image information of surrounding rock; receiving the image information of the surrounding rock; transforming and splicing pictures taken at different angles, to form a complete image reflecting the surrounding rock; recognizing and describing a surrounding rock feature of the complete image, and transmitting the annotated image to a virtual reconstruction module; displaying the complete image with a description of the surrounding rock feature by using a virtual reality device, to reflect surrounding rock situations at different angles/different positions. This has the advantages of high equipment automation, not prolonging the construction time, not requiring the sketcher to enter the tunnel, precise surrounding rock feature parameters, and supporting remote control.

A system for visually enhancing a surface. For example, the system may include an assembly that may be configured to project a laser line across at least a portion of the surface. Versions of the laser line may be projected at an angle of incidence relative to a plane defined by the surface. For example, the angle of incidence may be in a range of about 5 degrees to about 60 degrees. In one version, the laser line may be substantially perpendicular to a central axis of the surface. In addition, the laser line may enable enhanced, qualitative user visualization of the surface and non-planar distortion in the surface.

Systems and methods for analyzing image data to assess property damage are disclosed. According to certain aspects, a server may analyze segmented digital image data of a roof of a property using a convolutional neural network (CNN). The server may extract a set of features from a set of regions output by the CNN. Additionally, the server may analyze the set of features using an additional image model to generate a set of outputs indicative of a confidence level that actual hail damage is depicted in the set of regions.