Drone-Based Automated Yard Checks with ML Image Analysis
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Solution Overview
Problem
Accurately maintaining inventory in intermodal storage facilities is challenging due to discrepancies between assigned and actual container locations, with current systems failing to identify and correct user errors, leading to inefficient and time-consuming manual verification processes.
Innovation Solution
An unmanned aerial system (UAS)-based automated data collection system captures image data using an image capturing device, leveraging machine learning models to detect and identify objects, determine their locations, and generate results in near real-time, providing accurate inventory management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification of container locations is performed, then accuracy of inventory data is improved, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The patent replaces manual mechanical verification processes with an automated aerial imaging system that captures images of containers in storage facilities. The system uses image processing and pattern recognition algorithms to automatically identify container locations, types, and statuses, eliminating the need for manual inspection while providing accurate real-time inventory data.
Solution Approach 2:
The patent introduces an aerial imaging system as an intermediary between the physical container storage environment and the inventory management system. This intermediary captures visual data from above, processes it through image analysis algorithms, and translates it into structured inventory information, enabling automated monitoring without direct human intervention in the storage area.
2Productivity
If automated aerial imaging system is implemented, then productivity and speed of inventory monitoring are improved, but device complexity increases
Solution Approach 1:
The patent employs a multi-functional aerial imaging system that simultaneously performs multiple inventory monitoring tasks: capturing container locations, identifying container types, detecting storage conditions, and tracking movement. This single system replaces what would otherwise require multiple separate manual processes, achieving high productivity while managing complexity through consolidation of functions.
3Ease of operation
If current inventory control systems are used, then operational simplicity is maintained, but reliability and accuracy of location tracking deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the aerial imaging system continuously monitors container locations and compares detected positions with the inventory management system's recorded data. When discrepancies are detected (such as containers moved to wrong locations), the system automatically generates alerts and updates the inventory database, ensuring high reliability while maintaining ease of operation through automated correction.
Data Source
AI summary
Methods and systems for providing mechanisms for automated inventory control are provided. In embodiments, an operational workflow for providing automated inventory control includes automated data collection and automated image data analysis. The automated data collection includes capturing image data for a storage facility by a capturing device (e.g., an unmanned aerial system (UAS)). The automated image data analysis includes functionality to detect objects (e.g., containers, trailers, empty slots, and/or other objects) appearing in the image data (e.g., using a machine learning (ML) model), to identify the objects in the image data (e.g., using the ML model), to inspect the objects (e.g., including determining a condition and/or location of the objects) based on the collected image data and metadata associated with the image capturing device and correlated to the image data (e.g., using an advance mathematical rule-based analysis), and/or to generate results that may be used by an inventory management system.