Object Detection Network Adaptation via Sensor Interface Adapters
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Solution Overview
Problem
Existing object detection models for vehicles require retraining when environmental sensors are replaced or upgraded, necessitating acquisition and processing of new sensor data, which is time-consuming and inefficient.
Innovation Solution
A method involving adapter units and an object detection model with a trained neural network that processes sensor data from multiple environmental sensors, decoupling the model from specific sensor types and formats, allowing faster adaptation to new sensors by computing uniform interface data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the object detection model is trained on specific sensor data from environmental sensors, then the detection accuracy for that sensor type is improved, but the model must be retrained when sensors are replaced, increasing time and resource consumption
Solution Approach 1:
The patent introduces adapter units as intermediary components between environmental sensors and the object detection model. These adapters transform sensor-specific data into a standardized interface format, allowing the core detection model to remain unchanged while supporting multiple sensor types. This resolves the contradiction by maintaining detection accuracy through proper data transformation while eliminating retraining time through model reuse.
Solution Approach 2:
The system is segmented into distinct functional components: sensor-specific adapter units and a universal object detection model. Each adapter handles sensor-specific transformations while the shared model performs detection, allowing independent optimization and reuse. This segmentation enables the model to maintain high accuracy for each sensor type while avoiding redundant retraining.
2Adaptability or versatility
If the object detection model is adapted to individual environmental sensors, then the compatibility with that sensor type is improved, but the complexity of managing multiple sensor adaptations increases
Solution Approach 1:
The object detection model is designed as a universal component that can process data from multiple sensor types through standardized adapter units. Each adapter handles sensor-specific variations, allowing the core model to maintain high versatility across different sensors without requiring separate adaptations, thereby reducing management complexity.
Solution Approach 2:
Adapter units serve as intermediaries that handle all sensor-specific complexity, transforming diverse sensor outputs into a unified interface format. This shields the object detection model from sensor-specific variations, maintaining simplicity while achieving broad sensor compatibility through the standardized adaptation layer.
3Measurement precision
If new sensor data is acquired and processed before retraining the object detection model, then the model performance with new sensors is improved, but the productivity of deploying new sensors is reduced
Solution Approach 1:
Adapter units are designed and trained in advance for different sensor types, creating a library of pre-configured adapters. When new sensors are deployed, the appropriate pre-trained adapter is selected and connected to the existing model, eliminating the need for time-consuming data acquisition and retraining processes while maintaining detection performance.
Solution Approach 2:
Instead of retraining the entire detection model for each new sensor, the system copies and reuses the proven object detection model architecture, applying only the relevant pre-trained adapter for the new sensor type. This copying approach preserves detection performance while dramatically improving deployment efficiency by avoiding redundant training work.
Data Source
AI summary
A method for processing sensor data, specifying at least one environmental object, from multiple environmental sensors. The method includes providing first sensor data from a first environmental sensor, providing second sensor data from a second environmental sensor, providing a first adapter unit and a second adapter unit, providing an object detection model with at least one trained artificial neural network, inputting the first sensor data into the first adapter unit and computing first interface data that fulfill a specified interface data specification as output of the first adapter unit, inputting the second sensor data into the second adapter unit and computing second interface data that fulfill the interface data specification as output of the second adapter unit, inputting the first and second interface data as input data into the object detection model and computing at least one object parameter of the at least one environmental object as output.