Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2021 Nov 21;21(22):7746. doi: 10.3390/s21227746.
Real-time small infrared (IR) target detection is critical to the performance of the situational awareness system in high-altitude aircraft. However, current IR target detection systems are generally hardware-unfriendly and have difficulty in achieving a robust performance in datasets with clouds occupying a large proportion of the image background. In this paper, we present new results by using an efficient method that extracts the candidate targets in the pre-processing stage and fuses the local scale, blob-based contrast map and gradient map in the detection stage. We also developed mid-wave infrared (MWIR) and long-wave infrared (LWIR) cameras for data collection experiments and algorithm evaluations. Experimental results using both publicly available datasets and image sequences acquired by our cameras clearly demonstrated that the proposed method achieves high detection accuracy with the mean AUC being at least 22.3% higher than comparable methods, and the computational cost beating the other methods by a large margin.
实时小红外（IR）目标检测对于高空飞机态势感知系统的性能至关重要。然而，目前的红外目标检测系统通常对硬件不友好，并且在图像背景中云层占据很大比例的数据集上难以实现稳健的性能。在本文中，我们提出了一种新的方法，该方法在预处理阶段提取候选目标，并在检测阶段融合局部尺度、基于斑点的对比度图和梯度图。我们还开发了中波红外（MWIR）和长波红外（LWIR）相机进行数据采集实验和算法评估。使用公开数据集和我们相机获取的图像序列进行的实验结果清楚地表明，所提出的方法具有很高的检测精度，平均 AUC 至少比可比方法高 22.3%，并且计算成本也大大优于其他方法。
