Abstract
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.
Highlights
With the rapid development of computer vision and robotics, an enormous number of underwater operations can be conducted by underwater robots [1,2]
In this paper, aiming to reduce mismatch faced by underwater stereo matching, we mainly focus on further improving the Semi global matching (SGM) method by adopting two strategies: the first one is extracting the target area from the background with super-pixel segmentation and constraining the matching within the valid target area and the second is optimizing the basic disparity map within every super-pixel area by the least squares fitting interpolation method
The system does have a restriction on measuring distance, because we currently focus on improving the stereo matching, without thoroughly considering the problem of camera calibration caused by light refraction in air-glass-water transitions, which is another important issue
Summary
With the rapid development of computer vision and robotics, an enormous number of underwater operations can be conducted by underwater robots [1,2]. SGM methods adopt multiple paths optimization of disparity and achieve a minimum matching cost by the means of a winner-takes-all strategy based on hierarchical mutual information [33,34], which improves the calculation speed and effectively solves the mismatch problem caused by the uneven illumination in images [35]. It is a compromise strategy which is suitable for a real-time dense disparity map acquisition system based on binocular vision. The experimental results show that the proposed method can achieve higher measurement accuracy and better 3D reconstruction effect
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