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Hyperspectral Image-Based Night-Time Vehicle Light Detection Using Spectral Normalization and Distance Mapper for

Heekang Kim¹, Soon Kwon², Sungho Kim³

¹Department of Electronic Engineering, Yeungnam University, 280, Daehak-ro, Gyeongsan-si, Gyeongsangbuk-do KS011, Korea. kimhk@ynu.ac.kr.

Sensors (Basel, Switzerland)

|July 12, 2016

Summary

This study introduces a hyperspectral imaging method for accurate vehicle headlight detection, overcoming limitations of traditional cameras for intelligent headlight control systems. The approach effectively distinguishes various light sources, enhancing automotive safety.

Keywords:

Intelligent Headlight Control headlight detection hyperspectral image intelligent transportation system rear lamp detection spectral distance

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Area of Science:

Computer Vision
Automotive Engineering
Spectroscopy

Background:

Adaptive car headlamp control requires reliable vehicle light detection.
Traditional Charge-Coupled Device (CCD) or Complementary metal-Oxide-Semiconductor (CMOS) cameras struggle with distinguishing headlights from other light sources.
Existing Intelligent Headlight Control (IHC) methods face challenges like erroneous detection of streetlights, sign lights, and ego-car reflections.

Purpose of the Study:

To propose a novel vehicle light detection method for adaptive car headlamp control.
To overcome the limitations of CCD/CMOS cameras in detecting various automotive light sources (LED, HID, halogen).
To improve the accuracy and reliability of Intelligent Headlight Control (IHC) systems.

Main Methods:

Utilized hyperspectral imaging, offering hundreds of spectral bands for richer data compared to CCD/CMOS.
Applied spectral analysis techniques, including Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), and Euclidean Distance Mapper (EDM).
Evaluated the method's performance in detecting Light Emitting Diodes (LED), High-intensity discharge (HID), and halogen lights.

Main Results:

Hyperspectral imaging demonstrated superior information content over traditional cameras for light detection.
The proposed method successfully detected and differentiated between various types of vehicle lights (LED, HID, halogen).
Experimental results confirmed the feasibility and effectiveness of using hyperspectral images for IHC applications.

Conclusions:

Hyperspectral imaging presents a viable and robust solution for vehicle light detection in adaptive headlamp systems.
The proposed method significantly reduces erroneous detections common with CCD/CMOS-based systems.
This technology enhances the potential for more sophisticated and reliable Intelligent Headlight Control (IHC).