An infrared dim small target detection algorithm based on local product weighted contrast is proposed for the low detection rate and high false alarm rate of infrared dim small targets in complex backgrounds caused by pixel noise and high-bright edge interference. First, the mean value of the target area and the background area is calculated respectively, and the difference between target and local background is obtained. A local product weighting method is proposed, which greatly improves the salience of small targets and the suppression ability of background clutter. Second, multi-scale algorithm is used to enhance the adaptive ability of the algorithm. Finally, adaptive threshold segmentation is performed on the saliency image to obtain the real target to be detected. Simulation results show that compared with the existing algorithms, SCRg and BSF of the proposed algorithm are improved to a certain extent, and still have good accuracy and robustness under the complex background and strong noise interference, achieving the purpose of improving the detection rate and reducing the false alarm rate.