This research presents an innovative approach to pedestrian detection
in video surveillance, leveraging the power of YOLOv5 (You Only Look
Once version 5) combined with light perception fusion-based feature
extraction. The proposed methodology aims to enhance the accuracy
and efficiency of pedestrian detection systems in varying lighting
conditions. YOLOv5, known for its real-time object detection
capabilities, is integrated with a novel feature extraction technique that
fuses information from multiple light perception sensors. This fusion
strategy allows the model to adapt and perform robustly in diverse
lighting scenarios. The experimental results demonstrate the
superiority of the proposed method, achieving a remarkable
performance. The fusion of YOLOv5 with light perception-based
feature extraction showcases promising advancements in pedestrian
detection, addressing challenges posed by dynamic lighting conditions
in real-world surveillance environments.