# US 11,908,207 B2 · Method for detecting road diseases by intelligent cruise via unmanned aerial vehicle, unmanned aerial vehicle and detecting system therefor > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,908,207 B2 - Document ID: US11908207B2 - Status: Granted - Kind: B2 - Application number: 17/515,396 - Filed: 2021-10-29 - Published: 2022-07-28 - Granted: 2024-02-20 - Earliest claimed: 2020-11-02 - Assignees: BeSTDR Infrastructure Hospital (Pingyu); SAFEKEY Engineering Technology (Zhengzhou), Ltd. - Applicants: BeSTDR Infrastructure Hospital(Pingyu); SAFEKEY Engineering Technology (Zhengzhou), Ltd. - Inventors: Hongyuan Fang; Niannian Wang; Duo Ma; Juan Zhang; Jiaxiu Dong; Binghan Xue; Haobang Hu; Jianwei Lei - Web page: https://patentgrep.com/patent/US11908207B2 ## CPC - G06V 20/588: {Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road} - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/09: Supervised learning - G06V 10/98: Detection or correction of errors, e.g. by rescanning the pattern or by human intervention; Evaluation of the quality of the acquired patterns - G06V 20/17: taken from planes or by drones - G06V 20/176: {Urban or other man-made structures} - G06V 20/182: {Network patterns, e.g. roads or rivers} - B64U 10/13: Flying platforms - B64U 2101/30: for imaging, photography or videography - B64U 2201/10: autonomous, i.e. by navigating independently from ground or air stations, e.g. by using inertial navigation systems [INS] - Y02T 10/40: Engine management systems - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - B64U: UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR - Y02T: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION ## Abstract A method for detecting road diseases by intelligent cruise via an unmanned aerial vehicle (UAV), the UAV and a detecting system therefor are provided. The method for detecting road diseases by intelligent cruise via UAV, wherein a road disease detection model and a road recognition model based on deep learning network are built in the UAV, wherein the method specifically comprises a step of: automatically flying the UAV on a predetermined route on the actual road determined by the road recognition model, and obtaining road disease test results by the road surface disease detection model. The present invention adopts the road recognition model and road disease detection model based on deep learning network, which can realize automatic cruise and automatic road disease detection, only need to set a predetermined route or area range, which is convenient and fast. ## Claims ### Claim 1 A method for detecting road diseases by intelligent cruise via an unmanned aerial vehicle (UAV), wherein a road disease detection model and a road recognition model based on deep learning network are built in the UAV, wherein the method specifically comprises a step of: automatically flying the UAV on a predetermined route on the actual road determined by the road recognition model, and obtaining road disease test results by the road surface disease detection model. ### Claim 2 (depends on 1) The method for detecting road diseases by intelligent cruise via the UAV, as recited in claim 1, wherein the predetermined route is preset by the server; the UAV is communicating with the server to acquire the predetermined route data. ### Claim 3 (depends on 1) The method for detecting road diseases by intelligent cruise via the UAV, as recited in claim 1, wherein an operation mode of the UAV comprises intelligent flying, wherein the specific steps are: getting an environmental image; using the road recognition model to determine the road mark; and getting a predetermined route, drive the UAV to fly along the road surface on the predetermined route. ### Claim 4 (depends on 1) The method for detecting road diseases by intelligent cruise via the UAV, as recited in claim 1, wherein the operation mode of the UAV includes a fixed point cruise, and the specific steps are: determining several key locations of the scheduled route; and sequentially passing a critical position in accordance with the predetermined order by the UAV. ### Claim 5 (depends on 1) An unmanned aerial vehicle (UAV), comprising: a basic component, cameras and detection plates; wherein the camera, the detecting plates are connected to the basic component; the detection plate is loaded with a depth learning algorithm's road disease detection model and road recognition model, and can implement the road disease detection method as recited in claim 1. ### Claim 6 (depends on 5) The UAV as recited in claim 5, wherein the basic component comprises a rack, a motor, an electrical tone, a control board, a data transmission module, an image transmission module, an accelerometer, a positioning device; the electrical tone, the accelerometer, the positioning device, the detecting plate, respectively connect to the control board, integrated is mounted on the frame; the motor is connected to the electrical tone; the camera is connected to the image transmission module, the image transmission module and the data transmission module is respectively connected to the detection board. ### Claim 7 (depends on 1) A road surface disease detection system using the drone according to claim 1, comprising an interactive end and an UAV, wherein the UAV is in communication with the interactive end. ### Claim 8 (depends on 7) The road disease detection system by UAV intelligent cruise based on depth-learning, as recited in claim 7, wherein the interactive end is a computer or a mobile phone.