Abstract: bstract： 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 sensitive 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 applied to infrared small target detection.

Key words: Key words： infrared small target detection, feature extraction, local contrast, deep learning