Abstract: Infrared imaging technology is a passive detection technology that has been widely used in various fields such as land,sea,air and space,with the advantages of strong resistance to external interference and all-weather target measurement.Infrared imaging tracking and measurement systems have been used in a large number of applications in the measurement of surface vehicle parameters.Infrared sea surface small-target detection technology is an important part of processing infrared measurement images。The accuracy and efficiency of the interpretation results can be improved with suitable detection methods.By analyzing the characteristics of the background and target in the infrared sea surface small target image and the technical difficulties of the complex recognition of infrared sea surface small-targets,two infrared small-target algorithms based on deep learning and traditional methods are compared.The principle,steps and advantages of the traditional algorithms are elaborated,and the future development trend of infrared small-target detection algorithms is analyzed.

Key words: Infrared small-target detection, Tracking measurement, Image processing, Algorithm, Deep learning