可见光和红外图像决策级融合目标检测算法

An Object Detection Algorithm Based on Decision-Level Fusion of Visible and Infrared Images

为了提高可见光和红外图像决策级融合目标检测算法的性能,提出了一种基于模型可靠性的决策级融合策略。首先采取图像预处理技术提高红外图像的整体质量,之后对可见光与热红外目标检测模型分别进行训练测试,根据模型测试结果得到融合策略所需参数,依据所提出的融合策略对模型检测结果进行融合,得到最后的融合检测结果。实验结果表明,相比于单一目标检测模型的检测结果,所采用的融合算法在白天的漏检率比可见光检测模型降低了8.16%,夜间漏检率比红外检测模型降低了9.85%。

To improve the performance of visible and infrared image decision-making-level fusion target detection algorithms,a decision-level fusion strategy based on model reliability was proposed.First,image preprocessing technology was adopted to improve the overall quality of an infrared image,and then visible and thermal infrared target detection models were trained and tested.The parameters required for the fusion strategy were obtained based on the model test results.The model detection results were fused according to the proposed fusion strategy,and the final fusion detection results were obtained.The experimental results showed that compared with the detection results of a single-target detection model,the missed detection rate of the fusion algorithm used in the daytime was 8.16%lower than that of the visible detection model,and the missed detection rate at night was 9.85%lower than that of the infrared detection model.