Because of the low contrast and high noise intensity of underwater side-scan sonar images,the feature extraction ability of existing deep learning methods is still insufficient.An improved side-scan sonar multi-target recognition method is proposed based on PP-YOLOv2 by introducing attention mecha-nism.First,for the characteristics of side-scan sonar images with high signal-to-noise ratio and different image sizes generated by different sonar devices,several effective image preprocessing method are explored,including noise filtering,image data augmentation,etc.Secondly,based on PP-YOLOv2,which is a state-of-the-art target detection method with good performance both in terms of precision and efficiency,a new model is designed by replacing the backbone network with BotNet-DCN.By doing this,the atten-tion mechanism is introduced to improve the network feature improvement ability.Finally,a new decoupled head is designed to replace the original coupled head to perform refined prediction for small targets in side-scan sonar images.The results show that compared with the original PP-YOLOv2,the proposed method improves the average recognition accuracy by 4.4％;compared with the two mainstream methods based on convolutional neural network,the proposed method improves the average recognition accuracy by 4.66％and 5.42％,respectively,and improves the recognition efficiency by 32.4％and 27.6％,respectively.
关键词
水下侧扫声呐/多目标识别/PP-YOLOv2/图像预处理/注意力机制/解耦头
Key words
underwater side-scan sonar/multi-object recognition/PP-YOLOv2/attention mechanism/decoupled head
维普
万方数据