Object Detection System Using Pixel Block Histograms
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Solution Overview
Problem
Existing object detection systems for vehicles or robots face reliability issues due to distortion in images caused by dirt or rain on the windscreen, which can lead to inaccurate object recognition and increased production costs due to the need for additional distance measuring systems like radar.
Innovation Solution
An object detection system that analyzes images from image capture units by dividing them into pixel blocks, calculating distance information, and generating histograms with adjustable resolution based on reliability indices, allowing for reliable object detection without additional distance measuring systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a radar system is added to enhance detection reliability, then detection reliability is improved, but device complexity and cost increase
Solution Approach 1:
The patent divides the image into multiple pixel blocks and processes each block independently to calculate distance information. This segmentation allows the system to handle adverse weather conditions by processing only the reliable portions of the image, thereby maintaining detection reliability without adding radar systems.
Solution Approach 2:
The patent dynamically adjusts the resolution for histogram generation based on the reliability index of distance information. When reliability is low (adverse weather), the system changes parameters to generate histograms at lower resolution, which reduces the impact of noise and maintains detection accuracy without requiring additional hardware.
2Measurement precision
If image resolution is increased to improve detection precision, then measurement precision is improved, but reliability decreases under adverse weather conditions
Solution Approach 1:
The patent dynamically adjusts the histogram generation resolution based on the reliability index calculated from pixel block analysis. The system transitions from high-resolution processing in good weather to low-resolution processing in adverse weather, making the detection system adaptive to environmental conditions while maintaining reliability.
Solution Approach 2:
The system changes the resolution parameter for histogram generation according to the reliability index. When the reliability index indicates adverse weather conditions, the system reduces the resolution parameter, which suppresses the impact of noise and distortion while maintaining sufficient detection precision through the voting mechanism.
3Reliability
If clustering is applied to increase object recognition reliability, then object recognition reliability is improved, but productivity decreases when many clusters are invalid
Solution Approach 1:
The patent calculates a reliability index based on the statistical properties of distance information from pixel blocks and uses this feedback to dynamically adjust the histogram generation resolution. This feedback mechanism allows the system to identify and exclude invalid clusters caused by adverse weather while maintaining efficient processing by only processing reliable regions.
Solution Approach 2:
The system changes the histogram resolution parameter based on the reliability index to optimize the balance between recognition reliability and processing efficiency. When reliability is low, the system uses lower resolution histograms that are faster to process and less prone to invalid cluster formation, thereby maintaining productivity.
Data Source
AI summary
An object detection system is provided a plurality of image capture units for capturing images of surroundings of the system, a distance information calculation unit for dividing a captured image which constitutes a reference of captured images captured by the plurality of image capture units into a plurality of pixel blocks, individually retrieving corresponding pixel positions within the other captured image for the pixel blocks, and individually calculating distance information, and a histogram generation module for dividing a range image representing the individual distance information of the pixel blocks calculated by the distance information calculation unit into a plurality of segments having predetermined sizes, providing histograms relating to the distance information for the respective divided segments, and casting the distance information of the pixel blocks to the histograms of the respective segments.