一种新的决策级融合目标检测算法OA

To address the issue of poor object detection performance in visible images under low-light conditions,a decision-level fusion object detection algorithm for infrared and visible images based on YOLOv11 and a multilayer perceptron(MLP)model is proposed.This algorithm introduces an MLP model in the detection result fusion stage,which dynamically adjusts fusion weights through data learning to adapt to variations in input distribution,thus effectively improving the adaptability of the algorithm.Experiments on the LLVIP dataset demonstrate that compared with existing mainstream methods,the proposed YOLOv11-MLP-based decision-level fusion algorithm achieves the best performance on both evaluation metrics AP50 and AP50-95,while maintaining real-time performance comparable to that of mainstream methods.The experimental results indicate that the YOLOv11-MLP-based decision-level algorithm can effectively fuse the complementary information of visible and infrared images and improve object detection performance under low-light conditions.