Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China.
College of Internet of Things, Hohai University, Changzhou 213022, China.
Sensors (Basel). 2018 Oct 21;18(10):3570. doi: 10.3390/s18103570.
To better solve the problem of target detection in marine environment and to deal with the difficulty of 3D reconstruction of underwater target, a binocular vision-based underwater target detection and 3D reconstruction system is proposed in this paper. Two optical sensors are used as the vision of the system. Firstly, denoising and color restoration are performed on the image sequence acquired by the vision of the system and the underwater target is segmented and extracted according to the image saliency using the super-pixel segmentation method. Secondly, aiming to reduce mismatch, we improve the semi-global stereo matching method by strictly constraining the matching in the valid target area and then optimizing the basic disparity map within each super-pixel area using the least squares fitting interpolation method. Finally, based on the optimized disparity map, triangulation principle is used to calculate the three-dimensional data of the target and the 3D structure and color information of the target can be given by MeshLab. The experimental results show that for a specific size underwater target, the system can achieve higher measurement accuracy and better 3D reconstruction effect within a suitable distance.
为了更好地解决海洋环境下的目标检测问题，并应对水下目标三维重建的困难，本文提出了一种基于双目视觉的水下目标检测与三维重建系统。该系统使用两个光学传感器作为视觉传感器。首先，对系统视觉获取的图像序列进行去噪和颜色恢复，并根据图像显著度使用超像素分割方法对水下目标进行分割和提取。其次，为了减少不匹配，我们通过严格限制有效目标区域内的匹配，并使用最小二乘拟合插值方法对每个超像素区域内的基本视差图进行优化，从而改进半全局立体匹配方法。最后，基于优化的视差图，利用三角测量原理计算目标的三维数据，并通过 MeshLab 给出目标的三维结构和颜色信息。实验结果表明，对于特定尺寸的水下目标，该系统在适当的距离内可以实现更高的测量精度和更好的三维重建效果。
