Container Fullness Estimation via 3D Depth Histogram Analysis
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Solution Overview
Problem
Existing container loading systems face inefficiencies when dealing with large or irregularly shaped freight, such as gaylord boxes, which can leave unused space due to their dimensions and shape, affecting the accuracy of automated loading analytics and leading to wasted space.
Innovation Solution
A container monitoring unit (CMU) equipped with a 3D-depth camera and 2D camera captures images of the container's interior and exterior, generating depth data to create a histogram that estimates the container's fullness, allowing for improved space usage analysis and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional loading methods are used without computerized monitoring, then operational simplicity is maintained, but loading efficiency and space utilization cannot be accurately gauged
Solution Approach 1:
The patent replaces manual loading monitoring with an automated imaging system using cameras and image processing algorithms. The system captures images of container interiors, processes them through computer vision algorithms to detect freight placement and calculate space utilization, thereby substituting mechanical/manual monitoring with an automated optical system that provides continuous feedback on loading efficiency
Solution Approach 2:
The system enables self-service by automatically capturing images, processing them through histogram analysis, and generating loading efficiency reports without requiring manual intervention. The automated image processing system independently calculates space utilization metrics and provides feedback to operators, allowing the system to monitor and optimize its own performance
2Quantity of substance
If containers are filled to maximum capacity, then space utilization is improved, but measurement accuracy becomes difficult to maintain due to complex arrangements
Solution Approach 1:
The patent transitions from two-dimensional image capture to three-dimensional spatial analysis by generating depth maps and histograms from multiple image perspectives. The system creates a 3D representation of freight arrangement within the container, enabling accurate measurement of space utilization even when containers are filled to maximum capacity with complex freight configurations
Solution Approach 2:
The patent introduces histogram analysis as an intermediary between raw image data and fullness measurement. By converting complex 3D spatial data into simplified histogram representations, the system maintains measurement precision while accurately gauging container fullness, allowing differentiation between various loading scenarios even at maximum capacity
3Measurement precision
If automated image processing is implemented, then loading analytics accuracy is improved, but system complexity and processing requirements increase
Solution Approach 1:
The patent extracts only the essential features needed for loading analytics from complete 3D spatial data. By identifying and processing only the critical histogram parameters that indicate fullness and arrangement patterns, the system maintains high measurement precision while reducing processing complexity and computational requirements
Solution Approach 2:
The patent transforms complex spatial coordinates and 3D position data into simplified histogram parameters that capture the essential loading information. By changing the parameter representation from detailed 3D coordinates to aggregated histogram distributions, the system achieves accurate loading analytics with reduced computational 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
The CMU effectively estimates container fullness by analyzing depth data, providing accurate measurements and enhancing loading efficiency by identifying unused space and optimizing the stacking of freight, thereby reducing waste and improving loading capacity.
Implementation Method 1
A container monitoring unit (CMU) equipped with a 3D-depth camera and 2D camera captures images of the container's interior and exterior, generating depth data
Data Source
AI summary
Embodiments of the present invention are generally directed to estimating capacity usage of a container. In an embodiment, the present invention is a method of estimating a fullness of a container. The method includes: mounting an image capture apparatus proximate a container-loading area, the image capture apparatus operable to capture three-dimensional images; capturing, via the image capture apparatus, a three-dimensional image representative of a three-dimensional formation, the three-dimensional image having a plurality of points with three-dimensional point data including depth data; generating a histogram of the depth data from the three-dimensional image; and estimating the fullness of the container based at least in part on the histogram.