Aiming at the problem of insufficient robustness of existing target detection algorithms in surface garbage detection due to the interference of illumination, water ripple and reflection in images, a surface garbage significance detection method combining spatial prior information and frequency-domain phase spectrum was proposed. Based on background prior, local contrast prior and dark region prior information, the minimum obstacle distance map, contrast map and background map were fused in spatial domain to obtain the initial saliency map of surface garbage. In the frequency domain, the phase spectrum of the image is reweighted by low rank decomposition to obtain a significant target with less redundancy. Experimental results show that the accuracy of this method can reach 96.4%, and the interference of ripple, light and reflection can be effectively suppressed.