Abstract
Through the recognition of ocean sediment sonar images, the texture in the image can be classified, which provides an important basis for the classification of ocean sediment. Aiming at the problems of low efficiency, waste of human resources, and low accuracy in the traditional manual side-scan sonar image discrimination, this paper studies the application of image recognition technology in sonar image substrate texture discrimination, which is popular in many fields. At the same time, considering the scale complexity, diversity, multisources, and small sample characteristics of the marine sediment sonar image texture, the transfer learning is introduced into the image recognition, and the K-means clustering algorithm is used to reset the prior frame parameters to improve the speed and accuracy of image recognition. Through the experimental comparison between the original model and the new model based on transfer learning, the AP (average precision) value of the yolov3 model based on transfer learning can reach 84.39%, which is 0.97% higher than that of the original model, with considerable accuracy and room for improvement; it takes less than 0.2 seconds. This shows the applicability and development of image recognition technology in texture discrimination of bottom sonar images.
Highlights
Texture feature is the image feature with the most hidden information
It can express more information about the substrate in addition to its composition and structure. e side-scan sonar image can be used to distinguish the substrate texture, but the traditional sonar image discrimination method is too dependent on subjective consciousness, so the accuracy is not very high [3]. is paper presents the application of image recognition technology in texture discrimination of bottom sonar image. is study can greatly improve the weaknesses of low efficiency, low accuracy, and strong subjective consciousness of artificial discrimination of marine sediment side-scan sonar so as to provide sufficient data support and decision-making basis for further classification of marine sediment
Different from other convolution neural network classifiers using sliding windows, YOLO integrates target location, target region frame prediction, feature extraction, and classification into a single neural network model to realize end-to-end target recognition based on the deep convolution neural network [11]. e whole training and detection process, data input, and result output of YOLO are completed in the network, so it has better accuracy and faster recognition speed. e YOLO network consists of 24 convolution layers and 2 fully connected layers, in which the convolution layer is used to extract image features, and the fully connected layer is used to output prediction probability and location coordinates
Summary
Texture feature is the image feature with the most hidden information. Texture information shows different brightness or color in image features. Because the marine sediment is often the same kind of material in a large range, the texture of the sediment in the area may represent a kind of microtopography in the sonogram image. It may be a form of existence that cannot be displayed in the terrain but can have an impact on human activities; if this kind of texture features can be detected and recognized, the sediment information can be classified according to this kind of texture features. It can express more information about the substrate in addition to its composition and structure. e side-scan sonar image can be used to distinguish the substrate texture, but the traditional sonar image discrimination method is too dependent on subjective consciousness, so the accuracy is not very high [3]. is paper presents the application of image recognition technology in texture discrimination of bottom sonar image. is study can greatly improve the weaknesses of low efficiency, low accuracy, and strong subjective consciousness of artificial discrimination of marine sediment side-scan sonar so as to provide sufficient data support and decision-making basis for further classification of marine sediment
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