Vehicle Image Region Setting Using Weighted Corner Averaging
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Solution Overview
Problem
Existing image processing devices struggle to accurately set a vehicle image region in a frame image, particularly for detecting preceding vehicles, due to limitations in determining vehicle likelihood and corner coordinates.
Innovation Solution
An image processing device equipped with a search processor and an image region setting unit that calculates a vehicle degree for each processing region using machine learning techniques, performing weighted average calculations based on corner coordinates to determine the corner coordinates of a vehicle image region, thereby enhancing the accuracy of vehicle detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple region integration is used to set vehicle image regions, then device complexity is reduced, but measurement precision of vehicle detection deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/image-based region integration methods with a machine learning-based approach. The vehicle degree calculation unit uses trained models to evaluate the likelihood of each processing region containing a vehicle, substituting complex image analysis mechanics with learned patterns that achieve higher precision without proportional increases in device complexity.
Solution Approach 2:
The patent changes the parameter used for region evaluation from simple image features to a calculated 'vehicle degree' parameter. This parameter transformation allows the system to weigh multiple processing regions based on their vehicle likelihood, enabling precise vehicle region identification through weighted average calculations rather than simple integration.
2Measurement precision
If multiple processing regions are evaluated individually, then detection precision is improved, but loss of time increases due to multiple calculations
Solution Approach 1:
The patent performs preliminary action by pre-training the machine learning model offline before actual vehicle detection. The vehicle degree calculation unit uses this pre-trained model to quickly evaluate processing regions during runtime, avoiding the need for complex real-time image analysis and reducing processing time while maintaining high precision.
Solution Approach 2:
The patent uses a copied/trained model approach where the vehicle degree calculation unit applies a pre-trained evaluation model to multiple processing regions. This allows rapid replication of the evaluation process across different regions without repeating the full training computation, significantly reducing processing time while maintaining consistent precision standards.
3Manufacturing precision
If weighted average calculation with vehicle degree is performed, then manufacturing precision of region setting is improved, but device complexity increases due to additional calculation units
Solution Approach 1:
The patent implements a multi-functional vehicle degree calculation unit that performs both region evaluation and weighted average calculation functions. This universal unit consolidates multiple processing tasks into a single component, achieving high manufacturing precision for vehicle region setting while minimizing the increase in overall device complexity through functional integration.
Data Source
AI summary
An image processing device includes a search processor and an image region setting unit. The search processor sets a plurality of processing regions in a frame image, and calculates a vehicle degree with respect to each of the processing regions. The vehicle degree is a degree of vehicle likeliness of an image in a relevant one of the processing regions. The image region setting unit performs, on the basis of corner coordinates of four corners of each of the processing regions, weighted average calculation weighted with the vehicle degree with respect to each of the processing regions, to calculate corner coordinates of four corners of a vehicle image region including an image of a target vehicle, in the frame image.