Research on night vision pedestrian detection algorithm by incorporating attention mechanism

Abstract —Addressing the common challenges in night vision imagery — poor lighting conditions, low pixel resolution, and diminished contrast — which hinder effective pedestrian feature extraction and result in suboptimal accuracy and real-time performance for night-time pedestrian detection, This paper proposes a deep learning-based night vision pedestrian detection system. Building upon the YOLOv8 object detection algorithm, the model is enhanced by incorporating the CBAM attention mechanism into its network architecture and upgrading the optimiser from SGD to Lion. The system design and development are further tailored to address the specific characteristics of night-time imagery. After experimental simulation verification, the performance of the improved algorithm model has been significantly improved: the overall accuracy is improved by about 2.0%, [email protected] is improved by 1.6%, the average accuracy of IoU threshold 0.5 to 0.95 is improved by about 0.04%, and the F1 Score is improved by 0.64%. The improvement plan proposed in this paper effectively enhances the model's comprehensive identification ability of night vision pedestrians, improves the overall performance of the system, and verifies the correctness and validity of the research.