CN-121982590-A - Bridge eccentricity detection method

CN121982590ACN 121982590 ACN121982590 ACN 121982590ACN-121982590-A

Abstract

The invention relates to the technical field of railway bridge detection, in particular to a bridge eccentricity detection method, which is used for controlling an unmanned aerial vehicle to fly above a bridge, controlling a camera of the unmanned aerial vehicle to shoot a overlook image of the bridge, wherein the overlook image comprises two rails and one side of a blocking ballast wall edge, and calculating the bridge eccentricity through the proportion of the rails to pixels in the image and pixels in the image of the blocking ballast wall. According to the invention, the unmanned aerial vehicle is matched with the camera to detect the eccentric condition of the bridge in the running process of the railway bridge, so that the manual operation intensity is reduced.

Inventors

YIN JING
SHI LONG
MENG XIN
CHEN SHENGLI
WU SHENGTAO
WANG ZHEN
LI QINGCHI
MA HUIJUN
SU YONGHUA
WEI FENG
BAI BING
YUAN LEI
GUO HUI
Yan Wutong
Dou Junpeng

Assignees

中国铁道科学研究院集团有限公司
中国铁道科学研究院集团有限公司铁道建筑研究所
中国国家铁路集团有限公司

Dates

Publication Date: 20260505
Application Date: 20260129

Claims (8)

1. The bridge eccentricity detection method is characterized by comprising the following steps of: Controlling the unmanned aerial vehicle to fly above the bridge; Controlling a camera of the unmanned aerial vehicle to shoot a overlook image of the bridge; the overlooking image comprises two rails and one side ballast blocking wall edge; and calculating the bridge eccentricity through the proportion of the pixels of the rail and the ballasting wall in the image and the pixels of the two rails in the image.
2. The bridge eccentricity detection method according to claim 1, wherein the step of controlling the camera of the unmanned aerial vehicle to capture a top view image of the bridge comprises; And identifying key features of the overlook image according to the shot image, wherein the key features comprise rail edge contour features and ballasted wall edge contour features.
3. The method for detecting the eccentricity of a bridge according to claim 1, wherein the step of including two rails and one side ballast wall edge in the top view image includes: Shooting visual angles are aligned, and the flight attitude of the unmanned aerial vehicle is adjusted; Extracting edge characteristics of a reference object perpendicular to the rail in the image; And based on the reference object characteristics, shooting after the camera is calculated to be aligned with the front surface of the bridge.
4. The bridge eccentricity detection method according to claim 1, wherein a cradle head is installed between the unmanned aerial vehicle and the camera.
5. The bridge eccentricity detection method according to claim 1, wherein the bridge eccentricity calculation method comprises the following steps: ; Wherein, la is the distance between the center line of the bridge on one side and the ballasted blocking wall in the image, L is the designed distance between the center line of the bridge and the ballasted blocking wall, n 1 is the number of pixels between the edge of the ballasted blocking wall and the inner side of the track near one side of the ballasted blocking wall, and n 2 is the number of pixels between the edge of the ballasted blocking wall and the inner side of the track far from one side of the ballasted blocking wall.
6. The bridge eccentricity detection method according to claim 2, wherein the fixed end of the tripod head is mounted on the unmanned aerial vehicle, and the movable end of the tripod head is detachably connected with the camera.
7. The bridge eccentricity detection method according to claim 1, wherein the shooting distance of the camera is 6-20m.
8. The bridge eccentricity detection method according to claim 1, wherein the focal length of the camera is 34-477mm.

Description

Bridge eccentricity detection method Technical Field The invention relates to the technical field of railway bridge detection, in particular to a bridge eccentricity detection method. Background The bridge is an important structure crossing rivers, canyons and roads, plays a vital role in transportation junction, is an important component part in railway network especially for railways, is a controllable structure for guaranteeing safe operation, and has more self-evident importance for a plurality of large-scale railway bridges crossing Yangtze river and yellow river. Along with the progress of railway bridge construction technology and the large-scale construction of high-speed rails and passenger special lines, a series of new technologies, new processes, new materials and new structures are applied to bridge construction, so that the concepts of railway bridge design and construction are widened, a large number of large-span combined structures, arch bridges, cable-stayed bridges and suspension bridges with complex technologies and great design and construction difficulties are sequentially built into a general car, and accordingly later operation and maintenance management work of the bridge becomes heavier and more complex, and new challenges are brought to bridge maintenance and repair during operation. The existing railway bridge first-line management unit faces the situation that personnel are few, meanwhile, a plurality of bridge and culvert devices are newly added every year, the existing personnel are continuously scattered to the newly added devices, and then the personnel are continuously reduced, and a gap exists between on-site inspection maintenance personnel. In the aspects of equipment and equipment allocation, the equipment allocation of bridge maintenance equipment is insufficient, and novel technology and equipment related to bridge detection are lacked. Daily maintenance and railway maintenance are carried out in a 'skylight maintenance' mode, the skylight point time is very limited, generally 1.5-2.5 hours/time, and in addition, many bridges are too long in mileage, so that no time is required for checking and maintaining the bridge structure. Disclosure of Invention The invention aims to provide a bridge eccentricity detection method for solving the problems, and the condition of a bridge Liang Pianxin in the running process of a railway bridge is detected by matching an unmanned aerial vehicle with a camera. In order to achieve the above object, the present invention provides the following solutions: A bridge eccentricity detection method comprises the following steps: Controlling the unmanned aerial vehicle to fly above the bridge; Controlling a camera of the unmanned aerial vehicle to shoot a overlook image of the bridge; the overlooking image comprises two rails and one side ballast blocking wall edge; and calculating the bridge eccentricity through the proportion of the pixels of the rail and the ballasting wall in the image and the pixels of the two rails in the image. Preferably, the step of controlling the camera of the unmanned aerial vehicle to shoot the overlooking image of the bridge comprises the following steps of; And identifying key features of the overlook image according to the shot image, wherein the key features comprise rail edge contour features and ballasted wall edge contour features. Preferably, the step of looking down the image to include two rails and one side blocking wall edge includes: Shooting visual angles are aligned, and the flight attitude of the unmanned aerial vehicle is adjusted; Extracting edge characteristics of a reference object perpendicular to the rail in the image; And based on the reference object characteristics, shooting after the camera is calculated to be aligned with the front surface of the bridge. Preferably, a cradle head is installed between the unmanned aerial vehicle and the camera. Preferably, the method for calculating the bridge eccentricity comprises the following steps:; Wherein, la is the distance between the center line of the bridge on one side and the ballasted blocking wall in the image, L is the designed distance between the center line of the bridge and the ballasted blocking wall, n 1 is the number of pixels between the edge of the ballasted blocking wall and the inner side of the track near one side of the ballasted blocking wall, and n 2 is the number of pixels between the edge of the ballasted blocking wall and the inner side of the track far from one side of the ballasted blocking wall. Preferably, the fixed end of the cradle head is mounted on the unmanned aerial vehicle, and the movable end of the cradle head is detachably connected with the camera. Preferably, the shooting distance of the camera is 6-20m. Preferably, the focal length of the camera is 34-477mm. The invention has the following technical effects: according to the invention, the proportion of pixels in the imaging of the camera is compared wit