Vessel classification algorithm based on the convolutional attention module for natural maritime images

Abstract

In recent years, to ensure the stable development of the marine economy, the analysis and processing of maritime vessel targets combined with natural light images have played a significant role. However, in scenes such as ports, there are sometimes background noises similar to vessel features, so the classifier based on the convolutional neural network cannot achieve good classification results. To pay more attention to effective information when classifying natural maritime images, we propose an efficient channel attention module (ECAM) and a re-parameterized spatial attention module (RSAM) through one-dimensional convolution and re-parameterization method. This paper combines ECAM and RSAM to present an efficient re-parameterized convolutional block attention module (ER-CBAM) to classify natural maritime images. Besides, in response to the current lack of large-scale natural marine image datasets, we established a natural maritime vessel image (NMVI) dataset. Experiments on NMVI show that by combining the proposed attention module, ResNet50 can achieve a top-1 accuracy score of 85.32% with almost no extra computational consumption and a 3.02% improvement compared to the previous 82.30%, which suggests that the proposed method is suitable for maritime scenarios.