Automatic Recognition of Side-Scan Sonar Image for Underwater Target
In order to solve the limitations of side-scan sonar in detecting and recognizing of underwater target in the construction of current port,channel and Marine works,YOLOV3 deep learning method was applied,namely,deep neural network was trained and tested by using manually labeled side-scan sonar images in order to detect underwater wrecks.The transfer learning method was also adopted based on side-scan sonar data,that is,a pre-trained convolutional neural network was used to extract features,converge regions of interest(ROI),locate and classify objects,achieve automatic detection and recognition of objects,improve the working efficiency.The average recognition accuracy of detected objects was up to 88%.
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