VELIE: A Vehicle-Based Efficient Low-Light Image Enhancement Method for Intelligent Vehicles
Linwei Ye1,2, Dong Wang1,2, Dongyi Yang1,2
1Department of Electrical and Electronic Engineering, University of Liverpool, Liverpool L69 3BX, UK.
Sensors (Basel, Switzerland)
|February 24, 2024
Summary
This study introduces VELIE, a deep learning network for enhancing low-light driving images. It improves object detection and perception in challenging conditions, offering a cost-effective, real-time solution.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Automotive Engineering
Background:
- RGB cameras are crucial for Advanced Driving Assistance Systems (ADAS) and Automated Driving Systems (ADS) but struggle in low-light conditions.
- Existing solutions like multi-sensor fusion or specialized cameras are costly, hindering widespread adoption.
- Current low-light image enhancement methods lack detail, real-time capability, and edge deployment suitability for driving scenarios.
Purpose of the Study:
- To develop an efficient and cost-effective deep learning network for enhancing low-light images in automotive applications.
- To address the limitations of existing methods in detail enhancement and real-time processing for nighttime driving datasets.
- To enable robust environmental perception for ADAS and ADS under adverse lighting.
Main Methods:
- Leveraged the Swin Vision Transformer architecture.
- Integrated a gamma transformation with a U-Net for decoupled enhancement of low-light inputs.
- Developed a deep learning enhancement network named Vehicle-based Efficient Low-light Image Enhancement (VELIE).
Main Results:
- VELIE achieved state-of-the-art performance on various driving datasets.
- Demonstrated significant enhancement of high-dimensional environmental perception tasks in low-light conditions.
- Achieved a processing time of 0.19 seconds, enabling real-time inference.
Conclusions:
- VELIE offers an economical and effective solution for low-light image enhancement in automotive systems.
- The proposed network significantly improves the robustness and performance of ADAS and ADS in complex environments.
- VELIE facilitates edge deployment and real-time processing, paving the way for safer autonomous driving.
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