In order to efficiently and accurately filter out the influence of suspended particles such as cloud smoke on laser imaging fuze,a target recognition method based on the combination of improved Harris+ smallest univalue segment assimilating nucleus (SUSAN) corner detection algorithm and rectangularity was proposed. Based on the original Harris and SUSAN algorithms, the 8-neighborhood template standard deviation was used to perform the initial screening of target pixels to obtain candidate corner points, and after Gaussian filtering, the improved corner point response function value was used for secondary screening, and then the final corner point was obtained by non-maximum suppression, and finally the target and interference were quadratically distinguished by the rectangularity. Through theoretical analysis and experimental verification, 95% of the targets can be effectively identified. The results show that the proposed method can efficiently and accurately distinguish between target and interference, while meeting the real-time requirements, and provides a theoretical reference for the anti-interference of laser imaging fuze.