Computer-automated image-based inspection of an aircraft structure

A computer-implemented method, comprising:

• receiving a build plan for an aircraft structure specifying a plurality of expected locations of an expected plurality of holes, tacks, and/or fasteners on the aircraft structure;
• receiving a plurality of digital images of the aircraft structure from one or more cameras;
• generating a stitched digital image of the aircraft structure via a stitched digital image generation module based at least on the plurality of digital images and the build plan;
• determining a plurality of regions of interest (ROIs) of the stitched digital image;
• for each of the plurality of ROIs, inputting the corresponding ROI into an object detection machine learning (ML) model to thereby identify a plurality of actual locations of a plurality of actual holes, tacks, and/or fasteners on the aircraft structure in the corresponding ROI;
• generating a condition of assembly report for the aircraft structure that includes discrepancies between the plurality of actual locations and the plurality of expected locations;
• generating one or more event notifications for the aircraft structure based at least on the condition of assembly report, wherein the one or more event notifications indicate one or more remedial actions to be taken based at least on the discrepancies;
• and
• outputting the one or more event notifications.

A computing system, comprising:

• one or more processors configured to execute instructions stored in memory to:
• receive a build plan for an aircraft structure specifying a plurality of expected locations of an expected plurality of holes, tacks, and/or fasteners on the aircraft structure;
• receive a plurality of digital images of the aircraft structure from one or more cameras;
• generate a stitched digital image of the aircraft structure via a stitched digital image generation module based at least on the plurality of digital images and the build plan;
• input the stitched digital image into an object detection machine learning (ML) model to thereby identify a plurality of actual locations of a plurality of actual holes, tacks, and/or fasteners on the aircraft structure in the stitched digital image;
• generate a condition of assembly report for the aircraft structure that includes discrepancies between the plurality of actual locations and the plurality of expected locations;
• generate one or more event notifications for the aircraft structure based at least on the condition of assembly report, wherein the one or more event notifications indicate one or more remedial actions to be taken based at least on the discrepancies;
• and
• output the one or more event notifications.