Trailer Status Detection Using 3D and 2D Image Fusion
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Solution Overview
Problem
Traditional imaging systems in the commercial shipping industry struggle to accurately and efficiently determine trailer status due to noise in Time of Flight (ToF) sensor data from trailer materials, leading to errors in load metrics and analytics, as they cannot effectively utilize both 3D and 2D image data simultaneously.
Innovation Solution
A method and apparatus that capture both 3D and 2D images, using machine learning models to analyze the data and compare the results to determine a final trailer status, incorporating training on prior image data sets to improve accuracy and handle ambiguities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If traditional ToF sensors are used to capture 3D image data for trailer status determination, then depth information and spatial structure can be obtained, but abundant noise from trailer materials reduces measurement accuracy and detection reliability
Solution Approach 1:
The patent combines 3D image data from ToF sensors with 2D image data from conventional cameras to determine trailer status. By merging these two data sources, the system leverages the depth information from 3D data while using 2D data to compensate for noise-related inaccuracies, thereby improving overall measurement precision without sacrificing spatial information
Solution Approach 2:
The patent introduces 2D image data as an intermediary to resolve ambiguities in 3D data. When 3D data produces uncertain trailer status determinations due to noise, the corresponding 2D image provides additional contextual information that helps disambiguate the correct status, acting as a mediator to improve detection reliability
2Measurement precision
If 3D image data algorithms are used to determine trailer status, then depth-based detection is possible, but the system cannot effectively utilize 2D image data leading to failed ambiguity resolution
Solution Approach 1:
The system merges 3D and 2D image data processing pipelines, allowing both data types to be analyzed simultaneously for trailer status determination. This integration enables the system to maintain depth-based detection capabilities while also incorporating 2D image analysis, achieving versatile multi-data type utilization
Solution Approach 2:
The patent creates a universal trailer status determination system that can process both 3D and 2D image data through a unified algorithmic framework. This multi-functional approach allows the same system to leverage depth information from 3D data and contextual information from 2D data, adapting to different data types without requiring separate specialized systems
3Device complexity
If traditional systems determine trailer status using single data source, then processing is simpler, but errors occur such as mistaking partially pulled trailers for ajar doors or closed doors for open doors
Solution Approach 1:
The system uses feedback from both 3D and 2D data analysis to validate trailer status determinations. When one data source produces an ambiguous or potentially erroneous result, the other data source provides feedback to verify or correct the determination, reducing errors while maintaining manageable processing complexity through coordinated validation
Solution Approach 2:
The patent uses 2D image data as an intermediary verification layer for 3D-based trailer status determination. When 3D data suggests a particular status but creates ambiguity (such as distinguishing ajar from partially pulled trailers), the 2D image acts as a mediator to provide additional visual context that resolves the error without significantly increasing processing complexity
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy of trailer status determination, reducing errors and improving load metrics by combining 3D and 2D image analysis, thereby increasing the reliability of analytics and reducing processing inefficiencies.
Implementation Method 1
Time of Flight (ToF) sensors are frequently used to analyze the interior of shipping containers using three-dimensional (3D) and two-dimensional (2D) image data
Data Source
AI summary
Methods for determining a trailer status are disclosed herein. An example method includes capturing a three-dimensional image and a two-dimensional image. The three-dimensional image may comprise three-dimensional image data, and the two-dimensional image may comprise two-dimensional image data. The example method may further include determining a first trailer status based on the three-dimensional image data, and determining a second trailer status based on the two-dimensional image data. The example method may further include comparing the first trailer status to the second trailer status to determine a final trailer status.