Vehicle Tool Tracking Using AI Vision and RF Detection
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Solution Overview
Problem
Conventional tracking systems for tools in transportation vehicles face challenges with radio frequency signal obstructions, such as metal parts of tools or vehicle components, leading to compromised detection and tracking accuracy.
Innovation Solution
A tracking system that combines image-based detection using optical cameras and LIDAR sensors with RF signal-based detection, employing a cloud-based artificial neural network to predict tool identity and location, thereby enhancing detection precision and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If radio frequency signal-based tracking is used, then tracking coverage and range are improved, but detection precision deteriorates due to signal obstructions from metal parts
Solution Approach 1:
The patent combines RF signal-based tracking (providing wide coverage) with image-based detection using optical cameras and LIDAR sensors (providing high precision). The system integrates both detection methods to compensate for their individual weaknesses, achieving both broad coverage and accurate detection even in the presence of metal obstructions
Solution Approach 2:
The patent introduces optical cameras and LIDAR sensors as intermediary detection means that can penetrate or work around metal obstructions. These intermediaries provide alternative detection paths when RF signals are blocked, maintaining detection precision without sacrificing coverage
2Measurement precision
If multiple detection sensors are added to overcome signal obstructions, then detection precision is improved, but device complexity increases
Solution Approach 1:
The patent designs the detection system where optical cameras and LIDAR sensors serve multiple functions: they detect tool presence, determine tool location, and provide data for both immediate tracking and historical analysis. This multi-functionality reduces the need for separate specialized devices, managing complexity while maintaining precision
Solution Approach 2:
The system uses the existing transportation vehicle infrastructure (mounting points, power supply, processing units) to support the additional sensors. The vehicle's own resources are leveraged to accommodate the enhanced detection system, minimizing the need for external support structures and reducing overall system complexity
3Reliability
If image-based detection with neural networks is implemented, then false detection results are reduced, but processing time and computational resources increase
Solution Approach 1:
The patent employs a cloud-based artificial neural network that is pre-trained on extensive datasets of tool images and scenarios. This preliminary training allows the system to make rapid, accurate predictions during actual tracking operations, reducing real-time processing time while maintaining high reliability and minimizing false detections
Solution Approach 2:
The patent replaces traditional rule-based or threshold-based detection algorithms with an artificial neural network system. The neural network's ability to learn complex patterns and make probabilistic judgments provides more reliable detection with fewer false positives, even though it requires more computational resources, which are managed through cloud-based processing
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 high-precision tracking with reduced false detection results, ensuring reliable detection and localization of tools within and near the transportation vehicle, even in environments with significant metal obstructions.
Implementation Method 1
a vehicle-based detection unit having detection means for optically acquiring at least a portion of the plurality of tool sets at least in the vehicle loading space from different angles
Implementation Method 2
detection means for optically acquiring at least a portion of the plurality of tool sets at least in the vehicle loading space from different angles
Data Source
AI summary
A tracking system and method for tracking tools in and nearby a transportation vehicle is provided. The tracking system comprises a vehicle-based detection unit for optically acquiring tool sets in the vehicle loading space from different angles and providing digital image data as well as corresponding range information. An electronic main controller unit is operatively coupled to a communication receiving unit and communicates with the vehicle-based detection unit and a cloud-based computer system of the tracking system. A mobile computing unit includes an optical camera and a LIDAR sensor device and wirelessly communicates with the electronic main controller unit and the cloud-based computer system. The tool detection and tracking is accomplished by combining an image-based detection employing an artificial neural network in the cloud-based computer system and a signal-based detection employing the short-range wireless network communication means.