红外小目标检测方法综述

Review of Infrared Small Target Detection

本文旨在对基于传统的特征提取、局部对比与现今使用广泛的深度学习的3种红外小目标检测方法进行综述,并通过对比这3种方法的前沿应用,分析其在目标检测性能、鲁棒性和实时性等方面的优势和不足。从中发现,基于特征提取的方法在简单场景下具有较好的实时性和鲁棒性,但在复杂场景下可能受限。基于局部对比方法对目标的尺寸和形状变化相对鲁棒,但对背景干扰较为敏感。基于深度学习的方法在目标检测性能方面表现出色,但需要大量数据和较大的计算资源。因此,在实际应用中,应根据具体场景需求综合考虑这些方法的优缺点,并选择合适的方法进行红外小目标检测。

This article aims to review three infrared small target detection methods based on traditional feature extraction,local comparison,and widely used deep learning today.Then,by comparing the cutting-edge applications of these three methods,their advantages and disadvantages in target detection performance,robustness,and real-time performance are analyzed.We find that feature extraction based methods exhibit good real-time and robustness in simple scenarios,but may have limitations under complex conditions.The method based on local comparison is relatively robust to changes in object size and shape,but sen‐sitive to background interference.The method based on deep learning performs well in object detection performance,but requires large-scale data and larger computing resources.Therefore,in practical applications,the advantages and disadvantages of these methods should be comprehensively considered based on specific scenario requirements,and appropriate methods should be ap plied to infrared small target detection.