The detection of infrared small targets has been a challenging task in the field of computer vision due to the low percentage of small targets in the whole image and the presence of a large amount of clutter around the targets. We propose an algorithm based on associated directional gradient and mean contrast. The algorithm consists of two modules： the associated directional gradient module uses a Gaussian distribution model of infrared small targets， and adds the gradient in a single direction with the gradient in an adjacent direction to form a new feature called associated directional gradient， which enhances the real target， suppresses background clutter， and eliminates the effect of highlighting edges on the target detection. The mean contrast module incorporates directional information to calculate multi-directional contrast of the target. The minimum value of multi-directional contrast is chosen to suppress structural noise， and the idea of mean filtering is introduced into the calculation of contrast to suppress isolated noise in the background and further reduce the false alarm rate of detection. Experimental results on actual infrared images show that the algorithm can achieve better results in enhancing the signal-to-noise ratio of the target and suppressing the background noise.