Underwater Target Detection Method Based on Third Party Information Transfer Learning

The complex underwater environment leads to a small number of samples and a serious lack of target information. To solve the above problems, this paper proposes an underwater target detection method based on third party information transfer learning (TITL). Firstly, the method obtains the targets feature description from the knowledge base as third-party information. Using the third-party information and target information fusion, constitute a new target domain. Solved the problem of low detection accuracy caused by insufficient target data. Secondly, according to the uneven distribution of underwater image features, this method introduces the feature distribution difference adaptive principle into integrated migration learning. This method reduces the time of feature mapping and ensures the accuracy of migration learning. Experimental results show that the algorithm is effective in the Underwater Target dataset compared with existing algorithms.