Side-scan sonar image shipwreck detection based on lightweight YOLOv7 algorithm
For the existing side-scan sonar underwater shipwreck detection method,there are deficiencies in the detection speed and leakage detection in YOLOv5.This paper proposes an improved method for underwater wreck detection based on the lightweight YOLOv7 algorithm.First,the sampling numbers of shipwreck images are expanded by random flip,random noise and other operations.Second,a transfer learning strategy is introduced to transfer the weights learned on the COCO dataset to the YOLOv7 network for shipwreck detection.Third,the computation of the penalty term in the loss function of the model is improved to enhance the speed of convergence.Finally,a FasterNet structure is introduced into the YOLOv7 network,which reduces the number of parameters and the computational complexity of the model,and reduces the hardware requirement of the model to achieve lightweight model.The experimental results show that the improved method improves the class mean accuracy value(mAP value)by 4.75%compared with the original YOLOv7 algorithm,and the detection speed is also improved from 0.021 8 fps to 0.017 9 fps,which proves the value of the improved method in this paper for engineering applications.
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