Traffic Sign Pose Detection Using Odometer Optimization
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Solution Overview
Problem
Intelligent driving systems face significant detection errors in traffic signs, affecting safe vehicle driving due to complex and changing road environments, and limitations in camera technology such as monocular and binocular cameras.
Innovation Solution
A traffic sign detection method that determines an image frame sequence from a vehicle-mounted camera, uses odometer frame information to construct a residual function and optimization model, and predicts the target position and pose of the traffic sign or real-time distance between the camera and sign, thereby reducing detection errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle-mounted cameras (monocular or binocular) are used for traffic sign detection, then the system can perceive the surrounding environment, but significant detection errors occur in traffic sign position and distance
Solution Approach 1:
The patent introduces an optimization model as an intermediary between the camera detection and final traffic sign parameters. The model uses multiple frame images and odometer information as intermediate data to calculate traffic sign position and distance, reducing direct detection errors from single-frame camera limitations
Solution Approach 2:
The system performs preliminary actions by capturing multiple frame images before final detection and using odometer frame information in advance. The optimization model processes these pre-collected data to compute accurate traffic sign parameters, rather than relying on single-frame immediate detection
2Measurement precision
If multiple frame images and odometer information are processed through an optimization model, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The optimization model serves multiple functions simultaneously: it processes multiple frame images, integrates odometer frame information, calculates traffic sign position, and determines distance measurements. This multi-functionality consolidates what would otherwise require separate systems into a unified detection framework
Solution Approach 2:
The patent merges camera image data with odometer information in the optimization model. By combining these different data sources and processing them together through a single optimization framework, the system achieves accurate detection without requiring separate complex subsystems for each measurement type
Data Source
AI summary
A traffic sign detection method includes: determining an image frame sequence of the traffic sign captured by a vehicle-mounted camera for a vehicle, and odometer frame information corresponding to each frame image in the image frame sequence; determining a residual function and an optimization model based on each frame image and the odometer frame information corresponding to each frame image; determining a target position of the traffic sign in a preset coordinate system based on the residual function and the optimization model; and determining a target pose of the traffic sign or a real-time distance between the vehicle-mounted camera and the traffic sign based on the target position. Optimizing detection of a pose of the traffic sign or a distance between the vehicle-mounted camera and the traffic sign with frame images and the corresponding odometer frame information can reduce errors and improve accuracy.