Cargo Imaging System Using Neural Network Classification
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Solution Overview
Problem
Current methods for monitoring cargo in trailers are either time-consuming and require manual effort or are difficult to configure, such as manual inspection and acoustic sensor systems.
Innovation Solution
A cargo sensing system using an imaging device, like a camera, coupled with a digital signal processor and a learning classifier, captures and classifies images to determine the presence or absence of cargo, potentially identifying the type of cargo, through training images and artificial neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to monitor cargo, then cargo status can be determined, but it requires physical presence at the trailer and is time-consuming
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated imaging system. A camera captures images of the cargo space, and a processor analyzes these images to determine cargo status, eliminating the need for physical presence at the trailer while maintaining detection accuracy.
Solution Approach 2:
The patent creates a visual copy (image) of the cargo space instead of requiring direct physical inspection. The camera captures a representation of the cargo area, and this digital copy is then analyzed to determine cargo status, allowing remote monitoring without time loss.
2Ease of operation
If acoustic sensor systems are used to monitor cargo, then cargo status can be detected remotely, but they are difficult to configure and require manual adjustments
Solution Approach 1:
The patent implements a self-service system where the imaging device automatically captures images and the processor autonomously analyzes them to determine cargo status. The system performs manual inspection tasks automatically without requiring user configuration or adjustment, significantly improving ease of operation.
Solution Approach 2:
The patent replaces complex acoustic sensor systems with a simpler imaging-based system. Instead of using sensors that require careful configuration and manual adjustment, the system uses a camera and image processor that automatically perform cargo status detection without user intervention.
3Extent of automation
If imaging devices are used to capture cargo images, then remote monitoring is enabled, but image processing and classification require computational resources
Solution Approach 1:
The patent replaces manual inspection with an automated imaging and processing system. The camera captures images and the processor automatically analyzes them to determine cargo status, enabling remote monitoring while the computational energy requirement is managed through efficient image processing algorithms.
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
Provides a robust, efficient, and user-friendly method to monitor cargo status, reducing the need for manual adjustments and improving the accuracy of cargo detection, enabling remote monitoring and automatic reporting.
Implementation Method 1
A camera captures an image of a cargo space
Data Source
AI summary
A camera is used to sense cargo in a cargo space. An image of at least a portion of a cargo space is captured using the camera, and a digital signal processor classifies the image as representing an empty cargo space or a non-empty cargo space. Additionally, a type of cargo represented by the captured image may also be classified by the digital signal processor and an indication of the type of cargo output.