Aircraft Carry-On Bin Monitoring Using Computer Vision
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Solution Overview
Problem
Airlines face challenges in managing carry-on items in aircraft storage bins, leading to clogged aisles and boarding delays due to oversized or improperly oriented items, which can fill up storage space, necessitating the tagging and storage of additional luggage as checked items.
Innovation Solution
A system comprising cameras positioned to capture images of carry-on items and storage bins, with processors analyzing the data to determine item characteristics and alert conditions, such as oversized items or full bins, and sending alerts to boarding agents or crew members to manage item placement and storage capacity effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual monitoring of carry-on items is used, then device complexity is low, but productivity is reduced due to boarding delays and clogged aisles
Solution Approach 1:
The patent replaces manual mechanical monitoring with an automated computer vision system using cameras and image processing algorithms to detect carry-on items, their sizes, and storage bin occupancy, eliminating the need for manual inspection while improving boarding efficiency
Solution Approach 2:
The system enables self-service monitoring where the automated detection system continuously tracks storage bin status and alerts boarding agents without requiring manual intervention for each item check, allowing the system to monitor itself and trigger appropriate responses automatically
2Loss of time
If storage bins are not monitored, then device complexity is low, but loss of time increases due to boarding delays
Solution Approach 1:
The system performs preliminary detection and classification of carry-on items before they are placed in storage bins, using cameras to measure item dimensions and determine if they exceed allowed sizes, preventing time-consuming issues during boarding
Solution Approach 2:
The system continuously monitors storage bin occupancy levels and provides real-time feedback to boarding agents through alerts when bins approach capacity or when oversized items are detected, enabling timely responses to prevent boarding delays
3Measurement precision
If manual item size verification is performed, then measurement precision is sufficient for basic control, but productivity is reduced due to time-consuming inspections
Solution Approach 1:
The patent replaces manual measurement with automated computer vision technology that uses cameras and image processing algorithms to precisely measure carry-on item dimensions, achieving both high measurement precision and fast processing speeds simultaneously
Solution Approach 2:
The system changes the measurement approach from physical manual measurement to optical parameter analysis, using image data and machine learning models to determine item sizes, shapes, and volumes automatically, significantly improving processing speed while maintaining accuracy
4Object-generated harmful factors
If no monitoring system is used, then device complexity is low, but object-generated harmful factors increase due to oversized items blocking aisles
Solution Approach 1:
The system performs preliminary detection of oversized items using cameras before they can cause aisle blockages, classifying items by size and triggering alerts to boarding agents to prevent harmful situations before they occur
Solution Approach 2:
The system continuously monitors the aircraft interior and provides real-time feedback when oversized items are detected or when storage bins are full, enabling timely intervention to prevent aisle blockages and harmful situations
Data Source
AI summary
A system of monitoring carry-on items for a flight of an aircraft includes one or more cameras positioned to capture images of carry-on items associated with passengers of an aircraft. The system includes an interface configured to receive image data from the one or more cameras. The system also includes one or more processors coupled to the interface. The one or more processors are configured to analyze the image data to determine characteristics of a carry-on item. The one or more processors are configured to determine that one or more alert conditions are satisfied based on a comparison of one or more alert criteria to the characteristics of the carry-on item and data associated with the aircraft. The one or more processors are also configured to send, to one or more devices, an output based on satisfaction of the one or more alert conditions.