Applying the structure element (SE)-based morphology to detect the edges of the image corrupted by noise may obtain the irregular shape, and hence the precision of the object identification may decrease. Lately, the multi-SE morphology was introduced to detect the edge of gray-scale image, which yields the more integral and continual final result than that of the single-structure element. For color images, the robust color morphology gradient (RCMG) edge-detection, extended from CMG, was proposed by using a square SE along with the removing outliner technique to solve the noisesensitivity problem. In this paper, in order to solve such a noise problem on color images, we propose an efficient color edge-detection algorithm, called the ECMSEM (Efficient Color Multi-SE Morphology) by combining the advantage of the multi-SE morphology and the removing (noise) outliner. In our ECMSEM approach, the color multi-SE morphology with using a synthetic weighted method is introduced to detect only edge (except noise) and using multi-scale SEs to increase the precision of each SE-direction, and hence improve the edge-detection results. In performance evaluation, the edge-detection results (of synthetic color tested images) showed that our ECMSEM yielded the higher FOM than those of the RCMG approach.