A multi-scale surface target recognition algorithm based on attention fusion mechanism

Abstract

With the growing demand for marine environment supervision in China, surface target recognition has attracted more attention. To address the problems of complex water scenes with scale changes, much background information and inability to focus on key features, this paper proposes a multi-scale surface target recognition algorithm based on attention fusion mechanism. First, the network extracts different features from surface targets by multi-scale convolutional neural network. Then, discriminative features are enhanced by the fusion of channel attention module and spatial attention module. Finally, the feature representation of surface targets is formed by a joint loss function with localization loss and category loss. Tests are conducted on the VOC2007 dataset and the self-built surface target dataset, and the results show that the algorithm outperforms than other typical recognition on surface targets.