AI Blinking Light Detection Using Image Encoding Sequences
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Solution Overview
Problem
Current vehicle systems face challenges in accurately detecting whether a light source, such as a turn signal, is blinking due to insufficient image data and high computational costs, requiring specialized hardware and complex stateful logic.
Innovation Solution
A method utilizing AI and machine learning to generate encodings from image frames and analyze sequences of these encodings to determine if a light source is blinking, reducing data bandwidth and computational requirements by cropping images to focus on the light source and using two models: one for encoding and another for determining blinking status based on sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to detect blinking light sources, then detection capability is provided, but computational cost and data bandwidth requirements become excessively high
Solution Approach 1:
The patent extracts and processes only the relevant portion of the image containing the candidate light source rather than analyzing the entire image frame. This extraction approach significantly reduces computational complexity while maintaining detection accuracy by focusing resources on the critical detection area.
Solution Approach 2:
The patent segments the image processing task into distinct stages: first identifying candidate light sources, then extracting their bounding boxes, and finally analyzing only those extracted regions for blinking detection. This segmentation reduces overall computational burden by dividing the complex task into manageable steps with decreasing data volumes.
2Measurement precision
If high-resolution image frames are processed to improve detection accuracy, then measurement precision improves, but data storage requirements increase
Solution Approach 1:
The patent extracts only the bounding box regions containing candidate light sources from the full image frames, storing and processing only these extracted portions. This extraction dramatically reduces data storage requirements while preserving all necessary information for accurate blinking detection.
Solution Approach 2:
The patent segments the image data into full frames for initial candidate identification, then stores only the extracted bounding box regions for subsequent blinking analysis. This segmentation strategy maintains detection precision by preserving relevant light source information while minimizing overall data storage needs.
3Productivity
If specialized hardware is used to reduce computational costs, then device complexity increases, but processing efficiency improves
Solution Approach 1:
The patent replaces complex specialized hardware with a software-based processing pipeline that uses standard computing resources. The multi-stage processing approach with model-based analysis achieves high efficiency through algorithmic optimization rather than hardware specialization, maintaining flexibility while improving productivity.
Data Source
AI summary
This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method of image processing includes receiving a first image frame and determining a portion of the image frame that contains a candidate blinking light source. A model may be used to determine a first encoding based on the portion of the first image frame. A second model may use a a sequence of encodings to determine a lighting determination for the first candidate blinking light source. The sequence of encodings may include the first encoding. Other aspects and features are also claimed and described.