Vehicle Detection Apparatus Using Region-Specific Light Analysis
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Solution Overview
Problem
Existing vehicle detection systems face challenges in accurately and quickly detecting other vehicles based on captured images due to high processing loads and false detection issues when distinguishing between vehicle lights and stationary lights, which hampers real-time control of headlight light distribution.
Innovation Solution
A vehicle detection apparatus that includes an other-vehicle detection module, a vehicle lane-line detection module, and a region sectioning module to divide the image into specific regions, prioritizing detection conditions for points of light based on region type, such as prioritizing white lights for oncoming vehicles and red lights for vehicles ahead, while reducing sensitivity in exterior regions to minimize false detections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image analysis is performed on all points of light in the captured image to detect other vehicles, then detection accuracy can be maintained, but processing load increases extremely and detection speed decreases
Solution Approach 1:
The captured image is divided into multiple regions based on lane line detection results. Different detection conditions are applied to different regions: high sensitivity for oncoming vehicle lane region, medium sensitivity for own vehicle lane region, and low sensitivity for vehicle lane exterior region. This segmentation allows processing to focus on high-likelihood regions while reducing overall processing load.
Solution Approach 2:
Different detection conditions are assigned to different spatial regions of the image. The oncoming vehicle lane region uses conditions prioritizing white light detection, the own vehicle lane region uses conditions prioritizing red light detection, and the vehicle lane exterior region uses conditions with reduced sensitivity. This local differentiation optimizes both accuracy and processing efficiency.
2Measurement precision
If detection sensitivity is increased for all regions to improve vehicle detection accuracy, then more vehicles can be detected, but false detections of stationary lights increase
Solution Approach 1:
The image is segmented into regions with different probabilities of containing vehicles. By applying region-specific detection conditions, the system maintains high detection accuracy in relevant regions while reducing false detections in regions where stationary lights are more likely.
Solution Approach 2:
Detection sensitivity is locally adjusted for each region. The vehicle lane exterior region, where stationary lights are more common, uses reduced sensitivity conditions, while regions with higher vehicle presence probability use higher sensitivity conditions. This resolves the contradiction between detecting all vehicles and avoiding false detections.
3Measurement precision
If comprehensive image analysis is performed to distinguish vehicle lights from stationary lights, then detection accuracy improves, but processing time increases making real-time control difficult
Solution Approach 1:
Lane line detection is performed first to establish region boundaries before conducting point of light analysis. This preliminary action allows subsequent detection to be guided by pre-established spatial zones, reducing the scope of analysis needed and accelerating processing while maintaining discrimination accuracy.
Data Source
AI summary
A vehicle detection apparatus comprises an other-vehicle detection module configured to detect points of light in an image captured by a vehicle to which the vehicle detection module is mounted and to detect other vehicles based on the points of light, a vehicle lane-line detection module configured to detect an vehicle lane-line in the captured image, and a region sectioning module configured to section the captured image based on the detected vehicle lane-line into an own vehicle lane region, an oncoming vehicle lane region, and a vehicle lane exterior region. Other vehicles are detected by the other-vehicle detection module by detecting points of light based on respective detection conditions set for each of the sectioned regions.