An algorithm of ship image classification based on improved ResNet

Vessels as the main carrier of ocean transportation, when the ship is completed with intelligent transformation, the target detection and classification recognition problems of such ships will have an important impact on maritime traffic and national defense security. However, to enhance target detection performance, it often requires the use of larger and deeper neural networks, which in turn consume a substantial amount of computational resources and thus energy. This is particularly troubling for non-nuclear-powered ships that have limited energy resources. To address this issue, this paper presents an improved ship image classification method, the Dynamic Convolutional Network and Triplet Attention Mechanism Integrated ResNet (DY-ResNet-TA). Through theoretical analysis and experiments based on a ship image dataset, the proposed method has been proven to significantly improve model performance with a minimal increase in computational resource consumption.