Abstract: Disclosed herein are image-based object recognition method and system by and in which a learning server performs image-based object recognition based on the learning of environment variable data. The image-based object recognition method includes: receiving an image acquired through at least one camera, and segmenting the image on a per-frame basis; primarily learning labeling results for one or more objects in the image segmented on a per-frame basis; performing primary reasoning by performing object detection in the image through a model obtained as a result of the primary learning; performing data labeling based on the results of the primary reasoning, and performing secondary learning with weights allocated to respective boxes obtained by the primary reasoning and estimated as object regions; and finally detecting one or more objects in the image through a model generated as a result of the secondary learning.

Abstract: Disclosed herein is a method for an image analysis server to detect a change to a structure by using a drone. The method for an image analysis server to detect a change to a structure by using a drone includes: receiving images of a specific inspection target structure taken at different time points by a drone; detecting the difference between an image taken at a first time point and an image taken at a second time point based on the received images; and detecting a change to the inspection target structure via the detected difference, and generating a risk signal and then transmitting it to an administrator terminal.

Abstract: Disclosed herein are image-based object recognition method and system by and in which a learning server performs image-based object recognition based on the learning of environment variable data. The image-based object recognition method includes: receiving an image acquired through at least one camera, and segmenting the image on a per-frame basis; primarily learning labeling results for one or more objects in the image segmented on a per-frame basis; performing primary reasoning by performing object detection in the image through a model obtained as a result of the primary learning; performing data labeling based on the results of the primary reasoning, and performing secondary learning with weights allocated to respective boxes obtained by the primary reasoning and estimated as object regions; and finally detecting one or more objects in the image through a model generated as a result of the secondary learning.

Abstract: Disclosed herein is a method for an image analysis server to detect a change to a structure by using a drone. The method for an image analysis server to detect a change to a structure by using a drone includes: receiving images of a specific inspection target structure taken at different time points by a drone; detecting the difference between an image taken at a first time point and an image taken at a second time point based on the received images; and detecting a change to the inspection target structure via the detected difference, and generating a risk signal and then transmitting it to an administrator terminal.

Abstract: According to an aspect of the present invention, there is provided a method by which an analysis server determines a risk in a construction site based on image analysis, the method including the steps of: (a) acquiring an image, taken in a construction site, from an image acquisition device; (b) identifying one or more workers in the image, and grouping the workers based on identifiers that the respective workers have; and (c) determining whether protective equipment required to be worn for each group of workers has been correctly worn by analyzing the image.

Abstract: Disclosed herein is a method for an image analysis server to detect a change to a structure by using a drone. The method for an image analysis server to detect a change to a structure by using a drone includes: receiving images of a specific inspection target structure taken at different time points by a drone; detecting the difference between an image taken at a first time point and an image taken at a second time point based on the received images; and detecting a change to the inspection target structure via the detected difference, and generating a risk signal and then transmitting it to an administrator terminal.

Abstract: Disclosed herein is a system for inspecting equipment and materials for quality. The system for inspecting equipment and materials for quality includes: a magnetic sensor configured to generate a magnetic field in an inspection target object, and to detect magnetic flux density; and a quality inspection server configured to determine the presence of a defect, a portion where the detect is present, and the type of defect for the inspection target object based on magnetic flux density waveforms over a range from one end of the inspection target object to the other end thereof that are generated via signals detected by the magnetic sensor.

Abstract: Disclosed herein is a system for inspecting equipment and materials for quality. The system for inspecting equipment and materials for quality includes: a magnetic sensor configured to generate a magnetic field in an inspection target object, and to detect magnetic flux density; and a quality inspection server configured to determine the presence of a defect, a portion where the detect is present, and the type of defect for the inspection target object based on magnetic flux density waveforms over a range from one end of the inspection target object to the other end thereof that are generated via signals detected by the magnetic sensor.

Abstract: Disclosed herein is a coil sensor-based tensile force measurement system capable of temperature compensation. The coil sensor-based tensile force measurement system includes: a coil sensor configured to magnetize an inspection target object, and to detect information about a magnetic flux density formed by the magnetized inspection target object; a thermistor disposed to detect the temperature of a place where the coil sensor is installed; and a tensile force measurement server configured to acquire magnetic hysteresis curve information based on the temperature for the inspection target object, based on information collected by the coil sensor and the thermistor.