KR20230093086A - Drone system and measurement method for measuring bridge cracks - Google Patents

Drone system and measurement method for measuring bridge cracks Download PDF

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KR20230093086A
KR20230093086A KR1020210181141A KR20210181141A KR20230093086A KR 20230093086 A KR20230093086 A KR 20230093086A KR 1020210181141 A KR1020210181141 A KR 1020210181141A KR 20210181141 A KR20210181141 A KR 20210181141A KR 20230093086 A KR20230093086 A KR 20230093086A
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강호열
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Abstract

The present invention provides an intelligent crack detection drone system that can photograph bridges with a camera mounted on a drone, and compare the photographed image with a learned image to detect cracks and measure the size and length thereof. The intelligent crack detection drone system includes a drone; an image correction part; a detection part; an actual measurement part; and an output part.

Description

교량 균열 측정용 드론 시스템 및 그 측정 방법{Drone system and measurement method for measuring bridge cracks}Drone system and measurement method for measuring bridge cracks {Drone system and measurement method for measuring bridge cracks}

본 발명은 드론을 이용하여 촬영한 영상으로 교량의 균열을 감지하고, 측정하는 기술에 관한 것이다.The present invention relates to a technology for detecting and measuring cracks in a bridge with images taken using a drone.

드론은 무인 비행체로 원격 조정으로 비행하거나 지정된 경로를 따라 자율적으로 비행하는 비행체이다. 주로 군사적 용도로 활용되어 왔으나, 최근에는 운송, 보안, 레저 등 다양한 분야에서 활용되고 있다.A drone is an unmanned aerial vehicle that flies remotely or autonomously along a designated path. It has been mainly used for military purposes, but recently it has been used in various fields such as transportation, security, and leisure.

교량은 고속도로, 국도 등 다양한 도로에 설치되며 시간이 지나면서 노후화되며 이때 발생하는 균열 등을 방치 시 큰 사고로 이어지기 때문에 주기적으로 안전점검을 하고있가.Bridges are installed on various roads such as highways and national roads, and they deteriorate over time, and cracks that occur at this time can lead to major accidents if left unattended, so safety inspections are conducted periodically.

교량에 대한 안전 점검은 점검자가 직접 육안으로 확인하였으나 사람이 직접 접은 하기 어렵기 때문에 사다리차나 굴절차 혹은 다리에 직접 매달려 확인했다. 하지만 이런 경우 관측공간이 협소하여 정확한 관측을 할 수 없고, 안전사고의 위험에 노출되는 문제점이 있다.The safety inspection of the bridge was visually checked by the inspector, but since it was difficult for a person to directly fold it, it was checked directly by hanging on a ladder truck, articulated truck, or bridge. However, in this case, the observation space is narrow, so accurate observation cannot be performed, and there is a problem in that it is exposed to the risk of safety accidents.

이와 같은 이유로 최근에는 카메라를 드론에 장착하여 교량을 촬영하는 방법을 사용하고 있으나, 촬영된 영상을 재가공하고 해야 하는 과정이 있으며 그 영상을 판단하는 사람에 따라 다른 판단을 내릴 수 있어서 다시 직접 육안으로 확인해야 하는 등의 불편함과 어려움이 있다.For this reason, recently, a method of photographing bridges by attaching a camera to a drone is being used, but there is a process of reprocessing the recorded video, and different judgments can be made depending on the person who judges the video, so it can be directly visualized again. There are inconveniences and difficulties such as having to check.

본 발명은 교량을 드론에 장착된 카메라로 촬영하고, 촬영된 영상과 학습된 영상과 비교하여 균열을 검출하고 그 크기와 길이를 측정할 수 있는 지능형 균열 검출 드론 시스템을 제공한다.The present invention provides an intelligent crack detection drone system capable of photographing a bridge with a camera mounted on a drone, detecting a crack by comparing the captured image with a learned image, and measuring the size and length of the bridge.

본 발명이 해결하고자 하는 과제는 상기에서 언급한 것으로 제한되지 않으며, 언급되지 않은 또 다른 해결하고자 하는 과제는 아래의 기재들로부터 본 발명이 속하는 통상의 지식을 가진 자에 의해 명확하게 이해될 수 있을 것이다.The problems to be solved by the present invention are not limited to those mentioned above, and other problems to be solved that are not mentioned can be clearly understood by those skilled in the art from the description below. will be.

상술한 해결하고자 하는 과제를 해결하기 위해서 본 발명의 실시예에 따른 드론 시스템은 교량을 촬영하고, 촬영한 영상을 실시간으로 전송하는 드론부와 상기 드론으로 부터 전송된 교량의 영상에서 왜곡된 부분을 보정하는 영상보정부, 측정된 열화상 및 실화상을 분석하여 균열을 검출하는 검출부, 검출된 부분을 분석하여 실측하는 실측부, 실측된 데이터를 도면화 하는 출력부로 구성된다.In order to solve the above-described problem to be solved, the drone system according to an embodiment of the present invention captures a bridge and transmits the captured image in real time, and the distorted part in the image of the bridge transmitted from the drone. It consists of an image correction unit that corrects, a detection unit that analyzes the measured thermal image and real image to detect cracks, a measurement unit that analyzes the detected part and measures it, and an output unit that draws the actually measured data.

전술한 본 발명의 과제 해결 수단을 통해 교량에서 발생하는 균열을 검출하고, 검출된 균열의 크기와 폭을 측정하여 교량에 대한 안전점검을 수행할 수 있다. 또한, 기존 교량 촬영 영상과 비교하여 변화된 부분을 파악할 수 있는 기술적 효과가 있다.Through the above-described problem solving means of the present invention, it is possible to perform a safety inspection on a bridge by detecting cracks occurring in a bridge and measuring the size and width of the detected crack. In addition, there is a technical effect of identifying the changed part compared to the existing bridge photographed image.

도 1은 본 발명에 따른 시스템 구성 블록도이다.
도 2는 본 발명에 따른 시스템 작동 순서도이다.1 is a system configuration block diagram according to the present invention.
2 is a system operation flow chart according to the present invention.

교량을 열화상과 실화상 카메라를 동시에 촬영하고, 촬영된 영상을 실시간으로 전송하는 드론과; 상기 드론으로부터 수신된 영상 데이터를 실제 도면과 맞게 왜곡된 부분을 보정하는 영상보정부;와 측정된 영상을 분석하여 균열을 검출하는 검출부와; 검출된 부분을 분석하여 균열을 실측하는 실측부; 및 이때까지 가공된 데이터를 실측 도면화 하는 출력부를 포함하는 교량 균열 측정 드론시스템.A drone that simultaneously photographs the bridge with a thermal image and a real image camera and transmits the captured image in real time; An image correction unit for correcting the distorted part of the image data received from the drone to match the actual drawing; and a detection unit for detecting cracks by analyzing the measured image; a measurement unit that analyzes the detected portion and measures cracks; And a bridge crack measurement drone system including an output unit for drawing actual measurement data processed up to this point.

드론에 장착된 카메라에서 촬영된 영상은 실시간으로 전송되며 전송된 영상은 렌즈로 인한 왜곡이 발생하기 때문에 왜곡 없는 실측과 동일한 2D 영상으로 변환함. 이 영상들을 파노라마하여 맵을 완성함.The video taken by the camera mounted on the drone is transmitted in real time, and since the transmitted video is subject to distortion due to the lens, it is converted into a 2D video identical to the actual measurement without distortion. Panorama these images to complete the map.

열화상으로 균열의 유무를 확인하고, 균열 확인 시 실화상 영상에서 균열의 길이와 폭을 측정함. 이때 균열 확인은 딥러닝을 이용하여 추출하고, 설명가능 인공지능으로 균열로 추정한 이유를 텍스트로 설명함. 그리고 해당위치를 강조 표시 하고, 좌표와 함께 균열의 길이와 폭을 출력함. 이 내용을 도면과 함께 보고서 형태로 출력함.Check the existence of cracks with thermal images, and measure the length and width of cracks in real images when checking cracks. At this time, the confirmation of the crack was extracted using deep learning, and the reason why it was estimated as a crack with explainable artificial intelligence was explained in text. Then, the corresponding location is highlighted, and the length and width of the crack are output along with the coordinates. This content is output in the form of a report together with drawings.

Claims (3)

교량을 열화상과 실화상 카메라를 동시에 촬영하고, 촬영된 영상을 실시간으로 전송하는 드론과; 드론으로부터 수신된 영상 데이터를 실제 도면과 맞게 왜곡된 부분을 보정하는 영상보정부;와 측정된 영상을 분석하여 균열을 검출하는 검출부와; 검출된 부분을 분석하여 균열을 실측하는 실측부; 및 이때까지 가공된 데이터를 실측 도면화 하는 출력부를 포함하는 교량 균열 측정 드론시스템.
A drone that simultaneously photographs the bridge with a thermal image and a real image camera and transmits the captured image in real time; An image correction unit for correcting the distorted part of the image data received from the drone to match the actual drawing; and a detection unit for detecting cracks by analyzing the measured image; a measurement unit that analyzes the detected portion and measures cracks; And a bridge crack measurement drone system including an output unit for drawing actual measurement data processed up to this point.
 드론에 장착된 카메라에서 촬영된 영상은 실시간으로 전송되며 전송된 영상은 렌즈로 인한 왜곡이 발생하기 때문에 왜곡 없는 실측과 동일한 2D 영상으로 변환함. 이 영상들을 파노라마하여 맵을 완성함.
The video taken by the camera mounted on the drone is transmitted in real time, and since the transmitted video is subject to distortion due to the lens, it is converted into a 2D video identical to the actual measurement without distortion. Panorama these images to complete the map.
 열화상으로 균열의 유무를 확인하고, 균열 확인 시 실화상 영상에서 균열의 길이와 폭을 측정함. 이때 균열 확인은 딥러닝을 이용하여 추출하고, 설명가능 인공지능으로 균열로 추정한 이유를 텍스트로 설명함. 그리고 해당위치를 강조 표시 하고, 좌표와 함께 균열의 길이와 폭을 출력함. 이 내용을 도면과 함께 보고서 형태로 출력함.
Check the existence of cracks with thermal images, and measure the length and width of cracks in real images when checking cracks. At this time, the confirmation of the crack was extracted using deep learning, and the reason why it was estimated as a crack with explainable artificial intelligence was explained in text. Then, the corresponding location is highlighted, and the length and width of the crack are output along with the coordinates. This content is output in the form of a report together with drawings.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118090742A (en) * 2024-04-18 2024-05-28 江西赣粤高速公路股份有限公司 Image acquisition method for detecting hinge joint damage of prefabricated bridge

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118090742A (en) * 2024-04-18 2024-05-28 江西赣粤高速公路股份有限公司 Image acquisition method for detecting hinge joint damage of prefabricated bridge

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