基于YOLOv5和FCN-DenseNet水下图像多目标语义分割算法

带视觉系统的水下机器人作业离不开对水下目标准确的分割, 但水下环境复杂, 场景感知精度和识别精度不高等问题会严重影响目标分割算法的性能. 针对此问题本文提出了一种综合YOLOv5和FCN-DenseNet的多目标分割算法. 本算法以FCN-DenseNet算法为主要分割框架, YOLOv5算法为目标检测框架. 采用YOLOv5算法检测出每个种类目标所在位置; 然后输入针对不同类别的FCN-DenseNet语义分割网络, 实现多分支单目标语义分割, 最后融合分割结果实现多目标语义分割. 此外, 本文在Kaggle竞赛平台上的海底图片数据集上将所提算法与PSPNet算法和FCN-DenseNet算法两种经典的语义分割算法进行了实验对比. 结果表明本文所提的多目标图像语义分割算法与PSPNet算法相比, 在MIoU和IoU指标上分别提高了14.9%和11.6%; 与FCN-DenseNet算法在MIoU和IoU指标上分别提高了8%和7.7%, 更适合于水下图像分割.

Multi-object Semantic Segmentation Algorithm Based on YOLOv5 and FCN-DenseNet for Underwater Images

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.