Underwater robots with vision systems cannot operate without the accurate segmentation of underwater objects, but the complex underwater environment and low scene perception and recognition accuracy will seriously affect the performance of object segmentation algorithms. To solve this problem, this study proposes a multi-object segmentation algorithm combining YOLOv5 and FCN-DenseNet, with FCN-DenseNet as the main segmentation framework and YOLOv5 as the object detection framework. In this algorithm, YOLOv5 is employed to detect the locations of objects of each category, and FCN-DenseNet semantic segmentation networks for different categories are input to achieve multi-branch and single-object semantic segmentation. Finally, multi-object semantic segmentation is achieved by the fusion of the segmentation results. In addition, the proposed algorithm is compared with two classical semantic segmentation algorithms, namely, PSPNet and FCN-DenseNet, on the seabed image data set of the Kaggle competition platform. The results demonstrate that compared with PSPNet, the proposed multi-object image semantic segmentation algorithm is improved by 14.9% and 11.6% in MIoU and IoU, respectively. Compared with the results of FCN-DenseNet, MIoU and IoU are improved by 8% and 7.7%, respectively, which means the proposed algorithm is more suitable for underwater image segmentation.