Underwater vision research is the foundation of marine-related disciplines. The target contour extraction is of great significance to target tracking and visual information mining. Aiming at the problem that conventional active contour models cannot effectively extract the contours of salient targets in underwater images, we propose a dual-fusion active contour model with semantic information. First, the saliency images are introduced as semantic information, and extract salient target contours by fusing Chan–Vese and local binary ﬁtting models. Then, the original underwater images are used to supplement the missing contour information by using the local image ﬁtting. Compared with state-of-the-art contour extraction methods, our dual-fusion active contour model can effectively filter out background information and accurately extract salient target contours.