FUSION OF VISUAL AND INFRARED INFORMATION FOR NIGHTTIME PEDESTRIAN DETECTION
Keywords:
Pedestrian detection, Visual data, Deep learning, Advanced sensorsAbstract
Pedestrian detection has been a key area of research, particularly for enhancing road safety and aiding self driving vehicles. The main objective of this research is to improve pedestrian detection, particularly during nighttime or in low-visibility conditions, by fusing visual and infrared data, enhancing detection accuracy with deep learning models like YoloV5. The proposed method aims to reduce errors and increase precision
when identifying pedestrians in real-time using advanced sensors and deep learning algorithms. "Fusion of Visual and Infrared Information for Nighttime Pedestrian Detection" refers to combining visual (camera based) data with infrared (heat-sensing) data to detect pedestrians, particularly in challenging conditions such as nighttime driving.
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