# US 2023/0267753 A1 · Learning based system and method for visual docking guidance to detect new approaching aircraft types > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0267753 A1 - Document ID: US20230267753A1 - Status: Published application - Kind: A1 - Application number: 18/174,344 - Filed: 2023-02-24 - Published: 2023-08-24 - Earliest claimed: 2022-02-24 - Assignees: Honeywell International Inc. - Applicants: Honeywell International Inc. - Inventors: Debabrata PAL; Abhishek ALLADI; Bhavya Sri GADDE - Web page: https://patentgrep.com/patent/US20230267753A1 ## CPC - G06V 20/64: Three-dimensional [3D] objects - G08G 5/22: located on the ground - G06T 7/10: Segmentation; Edge detection (motion-based segmentation G06T7/215) - G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80) - G06V 10/764: using classification, e.g. of video objects - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - G08G 5/51: for control when on the ground, e.g. taxiing or rolling - G08G 5/55: for a single aircraft - G08G 5/727: {from a ground station} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93}) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract Automated visual docking guidance in and near a bridge area is described herein. One method for aircraft detection, including capturing camera image data of a new aircraft; generating a segmented aircraft mask; segmenting the image data of the new aircraft into body part segmentation data; classifying the body part segmentation data into a plurality of classes; analyzing each class of body part segmentation data to predict an aircraft type for the new aircraft; determining the aircraft type of the new aircraft based on the prediction analysis; and synthetic video generation of new aircraft for generating aircraft specific docking guidance for the new aircraft based on the determined aircraft type. ## Claims ### Claim 1 A method for aircraft detection, comprising: capturing camera image data of a new aircraft; generating a segmented aircraft mask; segmenting the image data of the new aircraft into body part segmentation data; classifying the body part segmentation data into a plurality of classes; analyzing each class of body part segmentation data to predict an aircraft type for the new aircraft; determining the aircraft type of the new aircraft based on the prediction analysis; and generating aircraft specific docking guidance for the new aircraft based on the determined aircraft type. ### Claim 2 (depends on 1) The method of claim 1, wherein segmenting the image data of the new aircraft includes retrieving the total number of pixels present in the mask. ### Claim 3 (depends on 2) The method of claim 2, wherein segmenting the image data of the new aircraft includes counting the total number of pixels associated with entire aircraft shape. ### Claim 4 (depends on 1) The method of claim 1, wherein if the total pixels are less than a threshold, then the subsequent method steps are skipped and the method begins again with a next video frame to determine when the total pixels are over the threshold indicating a clearly visible aircraft can be processed. ### Claim 5 (depends on 1) The method of claim 1, wherein segmenting the image data of the new aircraft includes predicting to which body part of the aircraft each pixel belongs. ### Claim 6 (depends on 1) The method of claim 1, wherein classifying the body part segmentation data includes classifying images of those body parts against a reference dataset of aircraft body parts. ### Claim 7 (depends on 1) The method of claim 1, wherein analyzing each class of body part segmentation data to predict an aircraft type for the new aircraft includes a plurality of prediction sub-engines and wherein the prediction sub-engines each specialize in predicting an aircraft type based on one classification factor and each prediction sub-engine makes a prediction based on the aircraft body part it is analyzing. ### Claim 8 (depends on 1) The method of claim 1, wherein the classification factor is selected from the group of factors including: engine shape, nose shape, wing shape, and tail shape. ### Claim 9 (depends on 1) The method of claim 1, wherein the classification factors include a weighting factor that values a particular classification factor over other classification factors. ### Claim 10 A method for aircraft detection, comprising: receiving camera image data of an aircraft and a scene having a number of non-aircraft elements within a field of view of a camera while the aircraft is approaching or in a bridge area of an airport; removing the aircraft from the scene; capturing camera image data of a new aircraft; generating a segmented aircraft mask; segmenting the image data of the new aircraft into body part segmentation data; classifying the body part segmentation data into a plurality of classes; analyzing each class of body part segmentation data to predict an aircraft type for the new aircraft; determining the aircraft type of the new aircraft based on the prediction analysis; and generating aircraft specific docking guidance for the new aircraft based on the determined aircraft type. ### Claim 11 (depends on 10) The method of claim 10, wherein generating aircraft specific docking guidance for the new aircraft incudes a direction to a particular lead-in line in the bridge area. ### Claim 12 (depends on 10) The method of claim 10, wherein generating aircraft specific docking guidance for the new aircraft incudes a direction to a particular stop line in the bridge area. ### Claim 13 (depends on 10) The method of claim 10, wherein segmenting the image data of the new aircraft into body part segmentation data includes one or more body part segments selected from the group including: engine shape, nose shape, wing shape, and tail shape. ### Claim 14 A method for generating a synthetic aircraft model, comprising: receiving camera image data of a scene having an existing aircraft and a number of non-aircraft elements within a field of view of a camera while the existing aircraft is approaching or in a bridge area of an airport; analyzing the camera image data of the existing aircraft to determine an aircraft type; determining a plurality of aircraft feature parameters; applying the plurality of aircraft feature parameters to a 3D new aircraft model; generating a 2D projection of the 3D new aircraft model; removing the existing aircraft from the scene in the camera image data leaving just the non-aircraft elements; merging the scene having just the non-aircraft elements and the 2D projection of the new aircraft to create a synthetic image of the new aircraft approaching or in the bridge area of the airport. ### Claim 15 (depends on 14) The method of claim 14, wherein non-aircraft elements are selected from the group including; ground crew equipment, ground crew vehicles, air bridges, parked aircraft, VDGS system components, stop lines, lead-in lines tarmac shape, and infield shape. ### Claim 16 (depends on 14) The method of claim 14, wherein determining a plurality of aircraft feature parameters includes two or more parameters selected from the group including; a rotation parameter, a scaling parameter, and a centroid position parameter. ### Claim 17 (depends on 14) The method of claim 14, wherein the method further includes varying brightness and contrast can be applied to the synthetic image to simulate daylight variation. ### Claim 18 (depends on 14) The method of claim 14, wherein the method further includes applying a photometric variance to the synthetic image. ### Claim 19 (depends on 14) The method of claim 14, wherein the method further includes applying a generative network variance to the synthetic image. ### Claim 20 (depends on 14) The method of claim 14, wherein the method further includes applying a climatic distribution to the synthetic image.