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This paper studies an advanced intelligent recognition method of underwater target based on unmanned underwater vehicle (UUV) vision system. This method is called kernel two-dimensional nonnegative matrix factorization (K2DNMF) which can further improve underwater operation capability of the UUV vision system. Our contributions can be summarized as follows: (1) K2DNMF intends to use the kernel method for the matrix factorization both on the column and row directions of the two-dimensional image data in order to transform the original low-dimensional space with nonlinearity into a higher dimensional space with linearity; (2) In the K2DNMF method, a good subspace approximation to the original data can be obtained by the orthogonal constraint on column basis matrix and row basis matrix; (3) The column basis matrix and row basis matrix can extract the feature information of underwater target images, and an effective classifier is designed to perform underwater target recognition; (4) A series of related experiments were performed on three sets of test samples collected by the UUV vision system, the experimental results demonstrate that K2DNMF has higher overall target detection accuracy than the traditional underwater target recognition methods.

Herein, we synthesized BiOBr/ultrathin g-C3N4/ternary heterostructures modified with black phosphorous quantum dots using a simple water bath heating and sonication method. The ternary heterostructure was then used for the photocatalytic degradation of tetracycline in visible light, with an efficiency as high as 92% after 3 h of irradiation. Thus, the photodegradation efficiency is greatly improved compared to that of ultrathin g-C3N4, BiOBr, and black phosphorous quantum dots alone. The synthesized ternary heterostructure improves the charge separation efficiency, thus increasing the photodegradation efficiency. This work provides a new and efficient method for the degradation of antibiotics in the environment.