False Positive Profile for Road Sign Detection Accuracy
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Solution Overview
Problem
Existing sign identification systems face challenges in reliably associating speed limit signs with the correct road links due to variances in sign locations and proximity of adjacent roads, leading to false positives, which can result in incorrect guidance and navigation issues.
Innovation Solution
A computer-implemented method and apparatus that generates a negative image or false positive profile for road links, identifying and storing locations of known or potential false positives, allowing cameras or sensors to ignore these areas during subsequent sign detection, thereby reducing incorrect associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sign detection is performed using cameras or sensors, then speed limit signs can be detected, but false positives occur due to variances in sign locations and proximity of adjacent roads
Solution Approach 1:
The system performs preliminary actions by generating a false positive profile that identifies potential false positive locations before actual sign detection occurs. This profile is created by analyzing historical detection data and map information to predict where false positives are likely to occur, allowing the system to preemptively filter out these areas during subsequent detection operations.
Solution Approach 2:
The false positive profile acts as an intermediary layer between the raw sensor data and the final sign detection results. This profile mediates the detection process by providing spatial information about problematic areas, allowing the system to intelligently filter detections based on their location relative to known false positive zones without completely disabling detection capability.
2Productivity
If sign detection coverage is expanded to capture more signs, then more speed limits are detected, but incorrect associations with road links increase
Solution Approach 1:
The system applies local quality by creating location-specific filtering rules based on the false positive profile. Different road segments have different false positive characteristics, and the system tailors its detection and filtering behavior to each specific location. This allows comprehensive detection coverage while maintaining high association accuracy by applying appropriate filters locally rather than using uniform detection parameters everywhere.
Data Source
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AI summary
Systems, methods, and apparatuses are described for a negative image or false positive profile for sign locations. Image data or another type of optical data is collected along a path by a collection device such as a camera. The data is analyzed to identify one or more false positive locations along the path at which signs for other paths may be detected. The false positive locations may be described in the negative image or false positive profile. Additional or subsequent optical data may be analyzed based on the negative image or false positive profile may be analyzed to identify at least one confirmed sign position.