Vehicle Object Recognition Using Adaptive Candidate Regions
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Solution Overview
Problem
Current object recognition systems for vehicles face challenges in accurately recognizing objects at high speeds in various adverse environments, such as night, snow, or rain, due to errors in localization and camera parameters, which affect the size and placement of candidate regions in images.
Innovation Solution
The method involves determining a candidate region in an image based on vehicle localization information and object information, using adaptive detection thresholds and region-based convolutional neural networks (R-CNN) to recognize objects, and enhancing image quality within the candidate region for improved recognition rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a default detection threshold is used for object recognition, then the recognition process is simple and fast, but the recognition accuracy decreases in adverse conditions
Solution Approach 1:
The detection threshold is made dynamic rather than fixed. The system adaptively adjusts the detection threshold based on the candidate region determined from localization information and object information, allowing the threshold to vary with operating conditions and candidate region characteristics to maintain high recognition accuracy across different scenarios
Solution Approach 2:
The system changes the detection threshold parameter based on the determined candidate region. By modifying this key parameter according to the spatial and contextual information from localization and object data, the system optimizes recognition accuracy for adverse conditions without requiring complete system redesign
2Reliability
If the candidate region size is increased to account for localization errors, then the object recognition coverage is improved, but the false detection rate increases
Solution Approach 1:
The system applies different processing and detection strategies to different regions. Within the determined candidate region, it uses an adaptive detection threshold tailored to that specific region's characteristics, rather than applying a uniform threshold across the entire image, thus balancing coverage and false detection rate
3Reliability
If image processing is applied to enhance candidate region quality, then the object recognition accuracy is improved, but the processing time increases
Solution Approach 1:
The system segments the image processing task by applying enhancement operations only to the determined candidate region rather than the entire image. This localized processing approach maintains recognition accuracy for the region of interest while significantly reducing the overall processing time and computational burden
Solution Approach 2:
The system applies image processing partially - only to the necessary candidate region identified through localization and object information, rather than processing the complete image. This partial action achieves the required recognition accuracy with reduced processing overhead
Data Source
AI summary
An object recognition method and apparatus are provided. The object recognition apparatus acquires localization information of a vehicle, acquires object information about an object located in front of the vehicle, determines a candidate region in which the object is predicted to exist in an image in front of the vehicle, based on the localization information and the object information, and recognizes the object in the image based on the candidate region.