Vehicle Object Recognition Error Feedback Loop
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Solution Overview
Problem
Deep learning methods for object recognition in vehicles face limitations in learning and accurately recognizing images with features different from those in their training datasets, leading to recognition errors and high costs associated with collecting large numbers of images.
Innovation Solution
An object recognition system and method that utilize a deep learning network within vehicles to identify recognition errors, transmit error data to a server, and update the network parameters, allowing for improved recognition accuracy without the need to collect all possible image data, using V2X, Bluetooth, and cellular communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If deep learning network is trained with limited image data, then training cost and time are reduced, but recognition accuracy for unseen features deteriorates
Solution Approach 1:
The system implements a feedback mechanism where recognition results are continuously compared with actual object information, and error cases are fed back to update the deep learning network parameters. This closed-loop feedback enables the system to learn from mistakes and improve recognition accuracy over time without requiring extensive pre-training data for all possible scenarios.
Solution Approach 2:
The deep learning network performs self-updates by automatically learning from recognition errors encountered during actual operation. The system serves itself by identifying its own knowledge gaps through comparison with ground truth data and autonomously adjusting its parameters to correct mistakes, eliminating the need for manual retraining with comprehensive datasets.
2Reliability
If deep learning network is updated with error data, then recognition accuracy is improved, but system complexity increases
Solution Approach 1:
The system automatically performs error detection, data transmission, parameter updates, and model retraining without human intervention. The deep learning network autonomously identifies recognition failures, requests updated parameters from the server, applies the updates, and continues operation with improved accuracy, thereby managing its own complexity through self-service mechanisms.
Solution Approach 2:
The system divides the complex task of maintaining recognition accuracy into separate functional modules: error detection module, data transmission module, parameter update module, and model retraining module. Each module handles a specific aspect of the improvement process, making the overall system more manageable and easier to implement despite the increased functionality required for continuous improvement.
Data Source
AI summary
An object recognition apparatus of a vehicle includes a communication device receiving information of an object from the object, a camera capturing an image of the object, and a controller that recognizes the image of the object based on a deep learning network, and, when information from recognizing the image of the object is compared with the information of the object and then the comparison result is determined as a recognition error, transmits the image of the object and an error determination result to a server.