A surveying system comprises a height measuring device and a high-low measuring device. The high-low measuring device comprises an object measured by the height measuring device, a distance measurement sensor which is provided in a known relationship with the object and measures a distance to a construction surface, a projecting device for projecting a high-low information, and an arithmetic control module, the arithmetic control module calculates a high-low information of the construction surface based on a height information of the object measured by the height measuring device and a distance information measured by the distance measurement sensor, and the projecting device projects the high-low information onto the construction surface.

A method and apparatus for identifying a concrete crack includes: obtaining a crack video, and manually annotating a video image frame by using a label; predicting a future frame and label for the annotated frame by using a spatial displacement convolutional block, propagating the future frame and label, to obtain a synthetic sample, and preprocessing the synthetic sample, to form a crack database; modifying input and output ports of data of a deep learning model for video semantic image segmentation and a parameter, to enable the deep learning model to accept video input, and establishing a concrete crack detection model based on the video output; using a convolutional layer in a trained deep learning model as an initial weight of the concrete crack detection model for migration; inputting the crack database into a migrated concrete crack detection model, and training the concrete crack detection model for crack data.

An information processing apparatus includes one or more processors that function as the following units: a detection unit configured to detect a defect occurring in a structure by using local information about the structure based on a first image of the structure; a first determination unit configured to determine a global state of the structure based on a result of detection of the defect by the detection unit and global information indicating information about a wider area than an area of the structure indicated by the local information, the global information being based on a second image having a lower resolution than that of the first image; and a second determination unit configured to determine a degree of damage to a predetermined area of the structure based on a result of determination made by the first determination unit of the global state of the structure.

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.

A computer system trains a machine learning model to estimate a real-world measurement of a feature of a structure. The machine learning model is trained using a plurality of digital image sets, wherein each image set depicts a particular structure, and a plurality of measurements, wherein each measurement is a measurement of a feature of a particular structure. After the machine learning model is trained, it is used to estimate a measurement of a feature of a particular structure depicted in a particular image set.

Provided are a structure inspection method and a structure inspection system capable of efficiently inspecting structure and predicting deterioration with high accuracy. The structure inspection method includes: acquiring information on a location having internal damage within an inspection target region; and imaging the inspection target region with a visible light camera a plurality of times while shifting an imaging location, wherein a location except for the location having the internal damage is imaged with first pixel resolution and the location having internal damage is imaged with second pixel resolution higher than the first pixel resolution. Damage appearing on a surface of the structure is detected on the basis of a visible light image captured by the visible light camera. Information on the location having internal damage within the inspection target region is acquired by capturing an image that visualizes an internal state of the inspection target region.

Systems and methods for selective reporting of construction errors 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 identify a construction error. The image data may be analyzed to identify a degree of the construction error. The identified degree of the construction error may be compared with a threshold selected based on an analysis of a construction plan associated with the construction site. In response to a first result of the comparison, a first severity may be assigned to the construction error, and in response to a second result of the comparison, a second severity may be assigned to the construction error, the second severity differs from the first severity. Further, information related to the construction error may be provided based on the severity assigned to the construction error.

Provided is a damage diagram creation support method, which includes: acquiring information on a region having internal damage to a structure within an inspection target region; acquiring a visible light image obtained by imaging the inspection target region with a visible light camera; detecting fissuring appearing on a surface of the structure in the visible light image; and creating a damage diagram in which the fissuring detected in the visible light image is traced. Also provided is a damage diagram creation support device capable of appropriately recording a detection result of fissuring automatically detected from an 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.

A non-transitory computer-readable recording medium recording an image processing program that causes a computer to execute processing of: specifying a damaged portion by analyzing a captured image of a construction; and predicting, in the captured image, a range to which damage spreads based on the specified damaged portion and design data associated with the construction.