Underwater occluded object recognition with two-stage image reconstruction strategy.

The complex underwater environment, such as foreign object occlusion and dim light, causes the feature of underwater objects to be seriously missing. And ripple causes deformation of objects, which greatly increases the difficulty of feature extraction. Existing object recognition models cannot accurately recognize obscured objects due to incomplete features of underwater objects. To solve the above problems, this paper proposes an underwater occlusion object recognition method based on two-stage image reconstruction strategy. Firstly, the salient feature extraction network and the relevant environment feature extraction network are constructed to extract the salient feature and the relevant environment feature respectively. Secondly, the two-stage image reconstruction model with gradient penalty constraints is constructed to obtain finely reconstructed images. Finally, the object recognition with feature adaptive boundary regression is constructed to realize the recognition of finely reconstructed images. To prove the effectiveness of the proposed algorithm, it is compared with the existing object recognition model in datasets with different levels of complexity. The average recognition accuracy of the proposed model is 78.36%, and the recognition rate is improved by 14.16% compared to the original image. Experiments show that the object recognition algorithm proposed in this paper is effective and superior to the existing algorithms. [ABSTRACT FROM AUTHOR]