Advanced Computer Vision for Baggage Identification

Highlights

The BagVision PoC 2.0 App was created to transform how airlines, baggage handlers, and passengers identify and manage baggage by integrating advanced computer vision (CV) features for both tagged and tag-less bags.

By improving the client’s existing applications, the app introduced powerful image recognition technology to streamline the process of tracking and managing baggage, even in complex airport environments.

The app was specifically designed to handle high-traffic environments, ensuring seamless baggage identification and management in busy airports.

The client is a global leader in air transport communication and IT, offering advanced solutions to airlines, airports, and governments. Operating in over 1,000 airports worldwide, the client’s technology enhances operations, boosts security, and streamlines passenger experiences. Known for its innovation in baggage handling, the company provides reliable, efficient services in air transport. To improve operational efficiency and reduce manual errors, they needed a solution that could automate and enhance the accuracy of bag identification using computer vision (CV) technology.

Employees: 4000+

Industry: Aviation

Sector: Baggage Handling, Airport Operations

Primary Objectives

Differentiation in the Baggage Industry:

Demonstrate and differentiate the computer vision-based baggage identification capabilities at key industry events.
Enhanced Baggage Enrolment:

Move from a single image capture process to a more robust method that takes multiple images from various angles or uses video during bag enrolment.
Fully Automated Identification:

Automate the assignment of baggage identification using both visual recognition and bag tag scanning, reducing the need for manual processes.
Operational Efficiency

Streamline baggage management processes such as re-tagging, re-flighting, and creating On-Hand (OHD) records in real-time.

Challenge

Limited Accuracy in Baggage Enrolment:

The initial system only allowed for single image capture during baggage enrolment, which reduced the accuracy of identifying bags effectively, especially in high-traffic environments.

Ineffective YOLO Object Detection Integration

The YOLO-based object detection model was inefficiently integrated, leading to delays and inaccurate baggage recognition, hindering system reliability.

Difficulty Identifying Bags in Piles:

The system struggled with identifying individual bags when they were part of larger piles, complicating baggage management.

Manual Processes Slowed Operations

A lack of automation in barcode scanning, re-tagging, and re-flighting resulted in slower operations and increased the risk of manual errors.

How Academy SMART Helped

Academy SMART approached the challenge by enhancing the existing Bag Manager Android app, incorporating the following critical improvements:

Multi-Angle Image Capture and CV Integration:

Academy SMART designed a system where users can capture multiple images of the baggage during enrolment. These images are processed using the YOLO object detection model, which provides more precise and reliable results than the previous solutions. This step ensures that baggage identification is accurate even when the bag is among many others. That allows to identify a bag as a standalone and also in the pile of bags.

Automated Bag Tag Barcode Recognition:

The solution integrated a real-time barcode recognition feature. By automating this process, users can scan the baggage tag as part of the enrolment process, which links the visual data to the LPN. This drastically reduces manual errors and enhances processing speed.

Backend System Integration:

Academy SMART implemented API integrations with the client’s Bag Manager, WorldTracer, and Bag Journey systems. These integrations allow the PoC app to access and update baggage histories, re-flight eligibility, and handle lost baggage cases. Users can query baggage data in real time, facilitating more efficient re-tagging and re-flight operations.

Streamlined Re-Tagging and Re-Flighting Operations:

Academy SMART streamlined re-tagging and re-flighting tasks by automating processes and integrating them into the existing system, reducing time and manual labor required for these tasks.

Bluetooth Printer Re-Tagging Functionality:

The app introduced Bluetooth printer support, allowing users to easily re-tag bags on the go, further improving operational efficiency for baggage handlers in busy airport environments.

Demo Mode for Passenger Terminal Expo:

To showcase the app’s features at the Passenger Terminal Expo, Academy SMART developed a demo mode that simulated real-world scenarios, demonstrating the full capabilities of the solution to industry stakeholders.

Business Impact

Exceeding Client Expectations with Bag Vision 2.0

Academy SMART delivered a fully functional demo of the Bag Vision 2.0 PoC within a tight deadline, integrating advanced computer vision models and barcode recognition. The result positioned the client’s solution as a groundbreaking tool for baggage management.
Foundation for Full-Scale Development

The success of the PoC has laid the groundwork for full-scale development, allowing the client to continue innovating in baggage handling and improving airport operations on a global scale.
Industry Showcase at Passenger Terminal Expo 2024

The Bag Vision 2.0 PoC was successfully presented at the Passenger Terminal Expo 2024, showcasing its advanced features and reinforcing the client’s leadership in baggage handling technology.

Summary

Technologies

YOLO

Angular

React Native

Services

Mobile Development

Computer Vision

Project Management

Quality Assurance

UI/UX Design

Team

Mobile Engineers

Front-End Engineers

QA Engineer

Project Manager

Data Scientist

UI/UX Designer

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Iryna Kurkina,

Chief Business Officer at Academy SMART

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Improving Baggage Identification with Advanced Computer Vision