Nighttime Vehicle Detection via Highlight Point Segmentation
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Solution Overview
Problem
Conventional vehicle detecting methods, especially at night, face high complexity and low detection efficiency due to dim light conditions, which reduces their accuracy and real-time performance.
Innovation Solution
A nighttime vehicle detecting method and system based on dynamic light intensity, utilizing a camera and computing unit to detect highlight points, perform optical flow filtering, and estimate distances, which includes steps like highlight detection, vehicle lamp judgment, and optical flow analysis to accurately identify vehicle lamps and calculate distances, while reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional front vehicle identifying methods using classifiers and deep learning algorithms are used, then vehicle detection accuracy can be improved, but computational complexity increases and real-time detection performance deteriorates
Solution Approach 1:
The patent segments the vehicle detection process into distinct modules: highlight point detection module, vehicle lamp judging module, optical flow filtering module, and distance estimating module. Each module performs a specific function, breaking down the complex deep learning task into simpler, more efficient sub-tasks that can be processed in real-time while maintaining detection accuracy.
2Reliability
If conventional front vehicle identifying methods are used, then vehicle detection capability is achieved, but detection efficiency decreases due to high complexity
Solution Approach 1:
The patent extracts and utilizes only the most critical features for vehicle detection - specifically highlight points from vehicle lamps - rather than processing entire images through complex classifiers. By focusing extraction on these key elements and using optical flow analysis specifically for moving vehicle lamps, the system achieves reliable detection with significantly improved efficiency.
3Measurement precision
If conventional vehicle detecting methods are used in nighttime conditions, then vehicle detection is possible, but detection efficiency and accuracy decrease due to dim light reducing visible features
Solution Approach 1:
The patent detects highlight points based on brightness intensity thresholds, effectively identifying the bright regions (vehicle lamps) against the dark nighttime background. This approach leverages the contrast between the illuminated vehicle lamps and the dim environment, allowing accurate detection despite low overall light conditions by focusing on the bright features that stand out.
Data Source
AI summary
A nighttime vehicle detecting method is for capturing an image by a camera and driving a computing unit to compute the image and then detect a highlight point of the image. The nighttime vehicle detecting method is for driving the computing unit to perform a communicating region labeling algorithm to label a plurality of highlight pixels connected to each other as a communicating region value, and then performing an area filtering algorithm to analyze an area of the highlight pixels connected to each other and judge whether the highlight pixels connected to each other are a vehicle lamp or not according to a size of the area. The nighttime vehicle detecting method is for driving the computing unit to perform an optical flow algorithm to obtain a speed of the vehicle lamp, and then filtering the vehicle lamp moved at the speed smaller than a predetermined speed.