Ocean Internal Wave Detection in SAR Images Based on Improved YOLOv7

Ocean internal waves not only enhance ocean mixing and impact sediment resuspension, but also threaten the safety of marine engineering facilities and underwater navigation bodies. Therefore, the accurate detection and identification of ocean internal waves are crucial to ensure the safety of marine activities and optimize the development of marine resources. To improve the accuracy and efficiency of ocean internal wave identification, this paper proposes an automatic identification technique based on YOLOv7, which can quickly and accurately extract the signatures of ocean internal waves from a large amounts of SAR (Synthetic Aperture Radar) images, and realize the efficient identification of ocean internal waves. First, in this paper, dynamic snake convolution (DSConv) is introduced into the efficient layer aggregation network (ELAN) module of the backbone network, so that the network can adaptively focus on the irregular strip-like morphology of the ocean internal waves. In addition, large separable kernel attention (LSKA) is introduced in Conv_BN_SiLU (CBS) of the two downsampling modules in the neck network to capture a wider range of contextual information and enhance the feature fusion process of ocean internal waves. The experimental results show that the F1, (mean average precision) mAP50, and mAP50:95 of the improved YOLOv7 network model are 91.3%, 94.3%, and 59.1%, respectively, which are 5.3%, 2.7%, and 2.8% higher compared to the baseline model.