Convolutional Neural Network Cargo Sensor for Soft Material Detection
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Solution Overview
Problem
Existing cargo sensor systems for trailers, relying on ultrasonic sensors and standard image processing algorithms, often provide false readings due to limitations in detecting soft cargo materials and unknown load conditions.
Innovation Solution
A camera-based optical cargo sensor system utilizing a convolutional neural network, optionally combined with a laser-ranging Time-of-Flight sensor, to accurately determine the cargo loading state within a container by processing images and generating reliable cargo information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ultrasonic sensors are used to detect cargo, then the system can detect cargo presence, but false readings occur with soft outer materials
Solution Approach 1:
The patent replaces ultrasonic sensors (acoustic/mechanical system) with optical sensors (camera-based imaging system). The optical sensor captures images of the cargo container interior, and a neural network processes these images to determine cargo loading status, eliminating the false readings issue with soft materials that plagues ultrasonic detection.
Solution Approach 2:
The patent changes the detection parameter from ultrasonic wave reflection (physical contact-based) to optical image analysis (visual-based). By using a neural network trained on image data, the system can identify cargo presence and loading status through visual characteristics rather than acoustic properties, improving reliability for soft materials.
2Productivity
If standard image processing algorithms are used, then the system can process images, but false results occur with unknown load conditions
Solution Approach 1:
The patent replaces standard image processing algorithms (rule-based systems) with a neural network (machine learning system). The neural network is trained on diverse image data including various load conditions, allowing it to accurately determine cargo loading status without producing false results, while maintaining efficient image processing capability.
Solution Approach 2:
The neural network is pre-trained on a comprehensive dataset of cargo images with various loading conditions, lighting scenarios, and cargo types. This preliminary training enables the network to automatically adapt to unknown load conditions without requiring real-time adjustments or rule-based logic, improving both accuracy and productivity.
3Device complexity
If a single sensor type is used, then the system is simpler, but accuracy is reduced
Solution Approach 1:
The patent combines multiple sensor types (optical camera sensor and laser-ranging Time-of-Flight sensor) into a unified cargo detection system. The optical sensor provides primary imaging data for neural network analysis, while the ToF sensor provides distance verification data. This merging of sensors improves accuracy through cross-validation without significantly increasing system complexity, as both sensors can be integrated into a single control architecture.
Solution Approach 2:
The system implements feedback by using the ToF sensor to verify the cargo readings determined by the optical sensor. The distance information from the ToF sensor provides feedback that confirms or corrects the loading status determination, creating a self-verifying system that improves reliability while maintaining manageable complexity through coordinated sensor operation.
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 system achieves higher accuracy in detecting cargo loading states, reducing false readings and improving operational efficiency by using machine learning to analyze images and verify results with secondary sensors.
Implementation Method 1
The optical sensor captures an image of an interior space within the cargo container
Implementation Method 2
a laser-ranging Time-of-Flight (ToF) sensor, that verifies the cargo reading determined by the optical cargo sensor
Data Source
AI summary
A cargo sensor system incorporates an optical cargo sensor to supply images to a convolutional neural network. In preferred embodiments, the neural network is implemented using a processor in a sensor module, and is trained by a machine learning system to determine the load state of the cargo container. Some embodiments also include a secondary sensor, such as a laser-ranging Time-of-Flight (ToF) sensor, that verifies the cargo reading determined by the optical cargo sensor.