To improve the accuracy and real-time performance of infrared (IR) dimsmall target detection, an IR dimsmall object detection algorithm based on an improved multi-scale fractal feature was presented.Computational analysis of the multi-scale fractal feature related to the fractal parameter K (MFFK), which was used for IR image enhancement in the algorithm,was performed. First, an improved multi-scalefractal feature (IMFFK) was presented to perform image enhancement after substituting the equation for computing fractal dimension into the equation for computing MFFK using the covering-blanket method. Thereafter, a computationally efficient IR dimsmall target detection algorithm was presented, in which the computation of IMFFK was simplified and an adaptive threshold was used to segment targets of interest from the background. Finally, the effect of primary parameters on image enhancement and computational cost was analyzed based on the simulation images. The IR real-world images were subsequently used to evaluate the detection performance of the proposed algorithm, and comparisons with state-of-the-art detection algorithms based on local contrast measureare performed. The proposed algorithm was capable of simultaneously detecting dimsmall and large targets in an IR image, irrespective of whether the targets were bright or dark, even though false alarms were detected in some scenarios. It is also capable of reachingapproximately 30 frames per second for low-resolution IR images (320×240). The proposed algorithm exhibitssatisfactory applicability and can be used to detect targets with high local contrast in an image.