Road Sign Detection Pipeline With Parallel Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in quickly detecting and understanding road signs, which can impact their operation and decision-making in real-time driving environments.
Innovation Solution
A unified sign detection and classification methodology is introduced, utilizing a generic sign detector and a robust parallel classification strategy. This approach includes sensor data from cameras and lidar, applying machine learning classifiers, and a text and symbol detector to accurately identify and classify road signs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a generic sign detector is used to identify road signs, then detection speed is improved, but classification accuracy may deteriorate
Solution Approach 1:
The system segments the sign detection and classification process into two distinct stages: a generic sign detector that quickly identifies potential signs, and multiple specialized classifiers that accurately categorize detected signs. This segmentation allows the fast generic detector to filter candidates while specialized classifiers handle precise classification, resolving the contradiction between speed and accuracy.
Solution Approach 2:
The generic sign detector acts as an intermediary component between the raw sensor data and the specialized classifiers. It pre-processes the data to identify potential sign regions, which then are passed to appropriate classifiers. This intermediary stage enables fast initial filtering while maintaining the capability for accurate classification through subsequent specialized processing.
2Measurement precision
If multiple specialized classifiers are used for accurate sign classification, then classification accuracy is improved, but system complexity deteriorates
Solution Approach 1:
The system implements a universal classification framework where multiple specialized classifiers operate under a unified architecture managed by the generic sign detector. This framework allows the system to maintain multiple specialized classification capabilities while presenting a single, manageable interface for sign detection, reducing the perceived complexity despite having multiple classifiers.
Solution Approach 2:
The system dynamically selects and activates only the necessary classifiers based on the detected sign type and context. Rather than running all classifiers simultaneously, the system adapts its classification pipeline to the specific detection needs, reducing computational complexity while maintaining high accuracy for each sign type.
3Reliability
If a robust parallel classification strategy is implemented, then classification reliability is improved, but processing time deteriorates
Solution Approach 1:
The system employs periodic action by implementing a two-stage classification process: first a quick generic detection pass, then selective application of specialized classifiers only when needed. This periodic approach ensures reliable classification through multiple checks while minimizing processing time by not applying full classification to every detected object.
Solution Approach 2:
The generic sign detector performs preliminary action by pre-identifying potential sign regions before applying specialized classifiers. This preliminary filtering reduces the number of cases requiring full classification processing, thereby maintaining high reliability through thorough classification where needed while reducing overall processing time through selective application.
Data Source
AI summary
The technology provides a sign detection and classification methodology. A unified pipeline approach incorporates generic sign detection with a robust parallel classification strategy. Sensor information such as camera imagery and lidar depth, intensity and height (elevation) information are applied to a sign detector module. This enables the system to detect the presence of a sign in a vehicle's external environment. A modular classification approach is applied to the detected sign. This includes selective application of one or more trained machine learning classifiers, as well as a text and symbol detector. Annotations help to tie the classification information together and to address any conflicts with different outputs from different classifiers. Identification of where the sign is in the vehicle's surrounding environment can provide contextual details. Identified signage can be associated with other objects in the vehicle's driving environment, which can be used to aid the vehicle in autonomous driving.