Object Detection Using Binary Pixel Descriptors for Vehicle Lights
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Solution Overview
Problem
Existing methods for headlight and traffic sign recognition in automotive applications face challenges in accurately distinguishing vehicle lights from other light sources and require significant computational resources, especially under varying weather conditions and limited hardware capabilities.
Innovation Solution
A computer-implemented method using a binary descriptor to analyze pixel information in images, allowing for efficient classification of light sources by binarizing and counting pixels along specific directions, reducing computational demand while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional pixel evaluation methods are used for headlight and traffic sign recognition, then recognition accuracy can be maintained, but computational resource requirements become excessively high
Solution Approach 1:
The patent extracts only the most critical pixel information (brightness values at specific locations and directions) from the entire image, rather than processing all pixels. By selecting and evaluating only these key pixels, the system maintains recognition accuracy while dramatically reducing computational load.
Solution Approach 2:
The patent segments the image analysis task by dividing pixels into specific sets based on their spatial relationships to the center pixel and their directional arrangements. This segmentation allows the system to process only relevant pixel groups (e.g., pixels in specific directions relative to the center) rather than the entire image data.
2Reliability
If all pixels in an image are evaluated for object detection, then detection completeness is improved, but processing time and computational complexity increase significantly
Solution Approach 1:
The patent extracts only the essential pixel information needed for detection (brightness values at specific locations and directions) while discarding redundant information from other pixels. This selective extraction maintains detection completeness for the target objects while significantly improving processing speed.
Solution Approach 2:
The patent applies partial action by evaluating only a subset of pixels (those in specific directional sets relative to the center) rather than all pixels in the image. This partial evaluation is sufficient for reliable detection while reducing processing time.
3Measurement precision
If detailed pixel analysis is performed to distinguish vehicle lights from other light sources, then classification accuracy improves, but hardware requirements increase
Solution Approach 1:
The patent extracts only the specific pixel characteristics needed for light source classification (brightness values at defined locations and directions), avoiding the need for complex analysis of all image data. This selective extraction achieves accurate classification with simpler hardware requirements.
Solution Approach 2:
The patent changes the approach from analyzing all pixel values to analyzing specific parameterized pixel sets (defined by their spatial relationship to the center and directional arrangement). This parameterization simplifies the hardware requirements while maintaining classification precision.
Data Source
Figure 1(a)~1(h)
Figure 2A
Figure 2B~2C
AI summary
A computer implemented method for object detection includes the following steps carried out by computer hardware components: acquiring an image; determining a pixel of the image as a base pixel; determining coordinates of a plurality of sets of target pixels, each set of target pixels including a plurality of pixels in a respective pre-determined relationship to the base pixel; for each of the sets of target pixels, determining information representing values of the pixels in the respective set of target pixels; and determining whether a pre-determined object is shown in the image based on the determined information.