A method of analyzing a composite plug includes creating a composite plug, where the composite plug includes a formation layer, a cement layer, and an interface region between them, and the cement extends into the formation sample in the interface region. The method further includes imaging the composite plug to gather a series of discrete images, where each discrete image in the series depicts a cross section of the composite plug and the discrete images are taken at set increments throughout a depth of the composite plug. The method further includes analyzing each discrete image in the series of discrete images to determine a porosity measurement of each discrete image, determine a first and second boundary of the interface region from the porosity measurement of each discrete image, and determine a depth of the interface region by a number of discrete images between the first boundary and the second boundary.

An image processing apparatus acquires a first image which captures a scene including an object from a first viewpoint position and a second image which captures a scene including the object from a second viewpoint position, and associates a coordinate position corresponding to a position of a feature of the object on the first image with a coordinate position corresponding to a position of a feature of the object on the second image. The image processing apparatus determines a partial region in the second image corresponding to a give region in the first image based on the association, generates a synthesized image by replacing an image of the given region using an image of the determined partial region, and superimposing variation data on the synthesized image.

In one embodiment, a method includes accessing an image of a damaged object. The method further includes determining, using a plurality of image segmentation models, a plurality of objects in the image. The method further includes determining, using a plurality of visual inference models and the determined plurality of objects from the image segmentation models, a repair-relevant property vector for the damaged object in the image. The repair-relevant property vector includes a plurality of damaged object properties. The method further includes generating a repair report using the repair-relevant property vector and a price catalogue. The repair report includes an indication of the damaged object and a price associated with the repair or replacement of the damaged object. The method further includes providing the generated report for display on an electronic display device.

Systems and methods for ranking entities using construction site images are provided. For example, image data captured from a construction site using at least one image sensor may be obtained. The image data may be analyzed to detect at least one element depicted in the image data and associated with an entity. The image data may be further analyzed to determine at least one property indicative of quality and associated with the at least one element. The at least one property may be used to generate a ranking of the entity. In some examples, the at least one property may be based on a discrepancy between a construction plan and the construction site, between a project schedule and the construction site, between a financial record and the construction site, between a progress record and the construction site, and so forth.

The present disclosure relates to a half-cast mark identification and damaged flatness evaluation and classification method for blastholes in tunnel blasting, including the following steps: S1-2: photographing first and second contrast images as well as a half-cast mark image after blasting; S3-6: performing denoising, gray-scale processing and binary processing on the above images, and identifying a boundary of a half-cast mark in each of the images; S7-9: determining a flatness damage variable, a quantitative relation among an area of a half-cast mark region, the damage variable and a fractal dimension, and a damage value of the half-cast mark image; S10-11: forming five-dimensional (5D) eigenvectors to obtain multi-dimensional digital information features of the images; and S12-13: selecting eigenvectors of 60 images as training data to input to a naive Bayes classifier (NBC), and taking eigenvectors of remaining 30 images as classification data to input the above well-trained NBC for classification.

A damage evaluation device, a method, and a program that can automatically evaluate a damage of an outer layer of a structure occurring with respect to construction of the structure are provided. In a damage evaluation device of a structure including a processor, the processor is configured to perform image acquisition processing of acquiring a captured image of the structure, perform damage detection processing of detecting damages (cracks) of the structure based on the acquired image, perform feature region detection processing of detecting a structure feature region (a region of a P cone mark) related to construction of the structure based on the acquired image, perform selection processing of selecting a specific damage (settlement crack) related to the detected structure feature region among the detected damages, and perform information output processing of outputting information about the selected specific damage. By outputting the information about the specific damage, the damage of the outer layer of the structure occurring with respect to the construction of the structure can be automatically evaluated, and application to validity verification of a construction method and improvement of the construction method can be made.

In an inspection support device that organizes a captured image including damage to a structure and includes a processor, the processor acquires image data including information regarding a structural drawing of a target structure on a medium, and damage identification information regarding the damage and captured image identification information regarding the captured image of the target structure having the damage that are added by a user on the medium, recognizes the damage identification information through image recognition from the image data, recognizes the captured image identification information through image recognition from the image data, associates the damage identification information corresponding to a predetermined damage and the captured image identification information for the target structure having the predetermined damage, acquires the captured image corresponding to the captured image identification information, and associates the damage identification information and the captured image with each other.

The degree of concrete surface roughness contributes to the bond strength between two concrete surfaces for either new construction or repair and retrofitting of concrete structures. Provided are novel systems and methods with industrial application to quantify concrete surface roughness from images which may be obtained from basic cameras or smartphones. A digital image processing system and method with a new index for concrete surface roughness based on the aggregate area-to-total surface area is provided. A machine learning method applying a combination of advanced techniques, including data augmentation and transfer learning, is utilized to categorize images based on the classification given during the learning process. Both methods compared favorably to a well-established method of 3D laser scanning.

A deterioration diagnosis device according to the present invention includes: a memory; and at least one processor coupled to the memory. The processor performs operations. The operations include: storing deterioration degree histories for portions of a structure to be diagnosed; acquiring reference information related to deterioration of the portions; generating a deterioration prediction model for predicting the deterioration degrees of the portions based on the histories and the reference information; predicting the deterioration degrees of the portions at a prediction time by using the generated deterioration prediction model; selecting, from among the portions, a portion where the deterioration degree predicted at the prediction time meets a predetermined condition; and outputting information relating to the selected portion and the predicted deterioration degree of the selected portion.

A method of determining macrotexture of an object is presented which includes obtaining a plurality of stereo images from an object, generating a local coordinate system for each image, detecting one or more local keypoints each having a local coordinate, generating a global coordinate system based on a plurality of ground control points (GCPs) with apriori position knowledge of each of the plurality of GCPs, transforming the one or more local keypoints in each image to one or more global keypoints each having a global coordinate, generating a sparse point cloud based on the one or more global keypoints, reconstructing a 3D dense point cloud of the object based on neighboring pixels of each of the one or more local keypoints and calculating the global coordinates of each pixel of the 3D dense point cloud, and obtaining the macrotexture based on the reconstructed 3D dense point cloud of the object.