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Artificial Reef Detection and Recognition Based on Faster-RCNN

Aiming at the problem of target detection of artificial reefs in the sea, the deep learning network Faster-RCNN and SSD are used to detect artificial reefs in the seabed images generated by multi-beam sonar grid data. Aiming at the problem of insufficient training samples, an AR21 data set was constructed. Based on this data set, the Faster-RCNN model and SSD model were trained using transfer learning, and verified on the test set. Experimental results show that the Faster-RCNN model has a detection accuracy of 95.53% and a recall rate of 98.83%. The SSD target detection model has an accuracy of 73.31% and a recall rate of 76.63%. The deep learning algorithm can identify artificial reefs more accurately and efficiently on multi-beam sonar images, and the Faster-RCNN model has higher accuracy, robustness and generalization capabilities.