Abstract: Aiming at the problems of unsatisfactory processing effect and long processing time in point target detection algorithm of single-frame infrared image with complex background, a hierarchical convolution filtering detection algorithm was proposed. It was mainly divided into two parts: firstly, according to the characteristics of small infrared targets, a hierarchical convolution filtering operator was designed to filter the image, so as to achieve the effect of efficiency increase and background suppression of small targets in the image. Secondly, the adaptive threshold method based on the maximum value was used to binarize the image to filter the background clutter, and finally extracted the target to be detected. Experiments in a large number of infrared images with different backgrounds show that the performance quantization results of background suppression factor and signal-to-noise ratio gain of the algorithm are better than that of the existing five typical infrared dim and small target detection algorithms, and the average processing time is only 30.42% of Laplacian of Gaussian (LoG) filtering algorithm. Through the experimental comparison, the hierarchical convolution filtering method can effectively solve the problem of small target detection in infrared images under different complex backgrounds.