As the origional Fast Hessian which is the most efficient blob feature detection algorithm can not meet requirements of those images in real-time applications to the target recognition, target tracking and so on, an accelerated Fast Hessian multi-scale blob feature detection algorithm is proposed to upgrade the detecting speed of the Fast Hessian. The basic idea of the proposed algorithm is to decrease the number of filter operations and to calculate selectively the values of sample points in the first and last scales for each Octave. Compared to the original one which calculates all the sample point values in these scales, the number of filter operations are distinctly decreased and the consuming time of detecting processing is also reduced. The experiments indicate that the accelerated Fast Hessian algorithm and the original one have the same detection results, but the implementation speed of the accelerated Fast Hessian is upgraded nearly 40% of the original one. It concludes that the accelerated algorithm is much more fit for real-time applications.