Hierarchical Container Recognition System for Automated Type Identification
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Solution Overview
Problem
Current container loading analytics rely on pre-provided container data, leading to latency and errors, and some customers may not have or be willing to share this data, making it difficult to accurately identify container types, especially in industries like air transport where containers can look similar.
Innovation Solution
A hierarchical system using machine learning, OCR, and 3D computer vision to automatically recognize container types from RGB-D images, with deep learning for visual differences and OCR/3D point cloud analysis for differentiation, and a temporal filter to correct recognition errors caused by occlusions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If container type is provided in advance by customer, then analytics accuracy is improved, but system independence deteriorates and customer data sharing requirement increases
Solution Approach 1:
The system performs self-service by automatically capturing images of containers and using machine learning models to identify container types without requiring customer input or pre-provided data. The container type recognition component autonomously processes images and determines container types, making the system independent of customer data sharing.
Solution Approach 2:
The patent replaces the manual/mechanical process of customers providing container type data with an automated optical system. RGB-D cameras capture images, and machine learning algorithms automatically recognize container types, substituting the need for human intervention and data sharing with automated computer vision technology.
2Measurement precision
If hierarchical recognition system is implemented, then recognition accuracy is improved, but system complexity increases
Solution Approach 1:
The recognition system is segmented into distinct hierarchical components: container recognition component for initial identification, character recognition component for reading container codes, and 3D point cloud component for geometric analysis. Each component handles specific aspects of container identification, allowing complex recognition tasks to be divided into manageable segments that work together.
Solution Approach 2:
The system introduces an intermediary container recognition component that sits between image capture and final container type determination. This intermediary processes images first, then routes to appropriate specialized components (character recognition or 3D analysis) based on initial assessment, mediating between raw data and final results to improve efficiency.
3Measurement precision
If multiple recognition components are used, then identification accuracy is improved, but processing time increases
Solution Approach 1:
The container recognition component performs preliminary action by initially processing all captured images to identify potential containers and their basic types. This preliminary processing filters out non-container objects and obvious cases, so that only ambiguous or complex cases require further analysis by character or 3D components, reducing overall processing time.
Solution Approach 2:
The system dynamically adjusts processing depth based on initial recognition results. For clearly identifiable containers, the system stops after basic recognition. For ambiguous cases, it dynamically activates additional components (character recognition or 3D point cloud analysis) only when needed, creating a flexible, adaptive processing pipeline that optimizes time versus accuracy.
Data Source
AI summary
Hierarchical systems and methods for automatic container type recognition from images are disclosed herein. An example embodiment includes a system for image analysis, comprising: a container recognition component; a character recognition component; and a 3D point cloud component; wherein the container recognition component is configured to receive an image and produce one of three outputs based on analysis of the image such that the output corresponds to either a container is identified, further analysis is performed by the character recognition component, or further analysis is performed by the 3D point cloud component.