3D Depth Imaging for Automatic Container Door Status Recognition
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Solution Overview
Problem
The transportation industry faces challenges in managing the diverse loading strategies and configurations of shipping containers, leading to inefficiencies in tracking loading progress and metrics across different personnel and facilities, particularly in large warehouses with tight shipping deadlines.
Innovation Solution
A 3D depth imaging system and method for automatic container door status recognition, utilizing a 3D-depth camera and machine learning techniques to accurately determine door statuses, including open, closed, and partially closed positions, across various container types, and configurations, enhancing efficiency and accuracy in container loading analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring techniques are used to track container door status, then personnel can directly observe and record loading status, but the system cannot provide updated status in real-time across large facilities with many containers
Solution Approach 1:
The patent replaces manual mechanical monitoring with an automated optical imaging system. A 3D depth imaging camera captures images of container doors, and image processing algorithms automatically determine door status (open/closed/partially closed). This substitution eliminates the need for manual observation and recording, providing real-time automated tracking across multiple containers simultaneously, thereby resolving the contradiction between measurement precision and productivity.
2Measurement precision
If 3D depth imaging and machine learning techniques are implemented for automatic door status recognition, then real-time tracking accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary image processing layer between the 3D depth camera and the door status determination. The system captures raw depth images, processes them through algorithms that analyze depth values and spatial relationships, and then determines door status. This intermediary processing layer simplifies the overall system architecture by breaking down the complex task into manageable steps: image capture, depth analysis, and status classification, thereby reducing system complexity while maintaining high accuracy.
3Loss of information
If manual monitoring is used in large facilities with tens of hundreds of containers, then personnel can individually assess each container, but the facility cannot track loading progress across all containers efficiently
Solution Approach 1:
The patent implements a universal imaging system that can monitor multiple container types and door configurations simultaneously. The 3D depth camera and image processing algorithms are designed to handle various container sizes, door positions, and opening states through a single integrated system. This multi-functional approach allows the facility to track loading progress across all containers efficiently, preventing information loss and reducing the time required compared to individual manual monitoring of each container.
Data Source
AI summary
Three-dimensional (3D) depth imaging systems and methods are disclosed for automatic container door status recognition. A 3D-depth camera captures 3D image data of a shipping container located in a predefined search space during a shipping container loading session. A container recognition application (app) receives the 3D image data, and determines therefrom a container point cloud representative of the shipping container, and a 3D image door area data subset defining one or more door areas of one or more corresponding doors of the shipping container. The container recognition app determines, from the 3D image door area data subset, a status type, of each of the one or more corresponding doors, selected from one of: (a) a door opened status type, (b) a door closed status type, or (c) a door partially closed status type.