Abstract
Enhancement of images from RGB cameras is of particular interest due to its wide range of ever-increasing applications such as medical imaging, satellite imaging, automated driving, etc. In autonomous driving, various techniques are used to enhance image quality under challenging lighting conditions. These include artificial augmentation to improve visibility in poor nighttime conditions, illumination-invariant imaging to reduce the impact of lighting variations, and shadow mitigation to ensure consistent image clarity in bright daylight. This paper proposes a pipeline for Shadow Erosion and Nighttime Adaptability in images for automated driving applications while preserving color and texture details. The Shadow Erosion and Nighttime Adaptability pipeline is compared to the widely used CLAHE technique and evaluated based on illumination uniformity and visual perception quality metrics. The results also demonstrate a significant improvement over CLAHE, enhancing a YOLO-based drivable area segmentation algorithm.
| Original language | English |
|---|---|
| Title of host publication | The IEEE INTELLIGENT VEHICLES SYMPOSIUM 2025 (IV) |
| Publication status | Accepted/In press - 11 Apr 2025 |
Fields of science
- 102003 Image processing
- 102001 Artificial intelligence
- 102002 Augmented reality
- 101016 Optimisation
- 502050 Business informatics
- 101015 Operations research
- 102029 Practical computer science
- 211911 Sustainable technologies
- 102021 Pervasive computing
- 502017 Logistics
- 303 Health Sciences
- 502 Economics
- 502028 Production management
- 303008 Ergonomics
- 211917 Technology assessment
- 102026 Virtual reality
- 501026 Psychology of perception
- 501025 Traffic psychology
- 102024 Usability research
- 102013 Human-computer interaction
- 202034 Control engineering
- 202003 Automation
- 211902 Assistive technologies
- 201306 Traffic telematics
- 502037 Location planning
- 201305 Traffic engineering
- 202031 Network engineering
- 202030 Communication engineering
- 102 Computer Sciences
- 102034 Cyber-physical systems
- 203 Mechanical Engineering
- 202040 Transmission technology
- 102019 Machine learning
- 211909 Energy technology
- 202 Electrical Engineering, Electronics, Information Engineering
- 202038 Telecommunications
- 211908 Energy research
- 202041 Computer engineering
- 501 Psychology
- 202037 Signal processing
- 102015 Information systems
- 202036 Sensor systems
- 501030 Cognitive science
- 202035 Robotics
- 203004 Automotive technology
JKU Focus areas
- Sustainable Development: Responsible Technologies and Management
- Digital Transformation
