Autonomous underwater vehicles (AUVs) rely on a mechanically scanned imaging sonar that is fixedly mounted on AUVs for underwater target barrier-avoiding and tracking. When underwater targets cross or approach each other, AUVs sometimes fail to track, or follow the wrong target because of the incorrect association of the multi-target. Therefore, a tracking method adopting the cloud-like model data association algorithm is presented in order to track underwater multiple targets. The clustering cloud-like model (CCM) not only combines the fuzziness and randomness of the qualitative concept, but also achieves the conversion of the quantitative values. Additionally, the nearest neighbor algorithm is also involved in finding the cluster center paired to each target trajectory, and the hardware architecture of AUVs is proposed. A sea trial adopting a mechanically scanned imaging sonar fixedly mounted on an AUV is carried out in order to verify the effectiveness of the proposed algorithm. Experiment results demonstrate that compared with the joint probabilistic data association (JPDA) and near neighbor data association (NNDA) algorithms, the new algorithm has the characteristic of more accurate clustering.
Panoramic stitching technology provides an effective solution for expanding visual detection range of the autonomous underwater vehicle. However, absorption and scattering of light in the water seriously deteriorate the underwater imaging in terms of distance and quality, especially the scattering sharply decreases the underwater image contrast and results in serious blur. This reduces the number of matching feature points between the underwater images to be stitched, while fewer matched points generated make image registration and stitching difficult. To solve the problem, a joint framework is established, which firstly involves a convolutional neural network-like algorithm composed of a symmetric convolution and deconvolution framework for underwater image enhancement. Then, it proposes an improved convolutional neural network-random sample consensus method based on VGGNet-16 framework to generate more correct matching feature points for image registration. The fusion method based on Laplacian pyramid is applied to eliminate artificial stitching traces and correct the position of stitching seam. Experimental results indicate that the proposed framework can restore the color and detail information of underwater images and generate more effective and sufficient matching feature points for underwater sequence images stitching.
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