Error correction in underwater topographic survey based on convolutional neural network
The accuracy of underwater topographic survey is of great significance to the judgment of under-water topography and navigation safety.The survey error directly affects the judgments of underwater topography.Therefore,the method for error correction in underwater topographic survey is proposed in this paper based on convolutional neural network,it can automatically identify and correct the errors in underwater topographic survey data,and improve the accuracy of underwater topographic survey.Firstly,the characteristic parameters of underwater topographic survey are counted according to the principles of underwater topographic survey,and the feature vectors established.Then it is used as the input based on the improved convolutional neural network to generate a new underwater topographic survey image through continuous learning and training.Finally,the multi-scale attention mechanism is introduced to refine the measurement image space,and the similarity between the survey image and the label image is calculated.The parameters in the process of underwater topographic survey image generation are corrected according to the maximum image similarity calculation results.The results show that the error is less than 1.7％when it is corrected,it can effectively correct the distortion and error,accurately survey the underwater terrain,and the surveyed results are highly consistent with the real conditions.
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