Rotated target recognition of sonar images via convolutional neural networks with rotated inputs

Rotated target recognition is a challenge for Convolutional Neural Networks (CNN), and the current solution is to make CNN rotational invariant through data augmentation. However, data augmentation makes CNN easy to overfit small scale sonar image datasets, and increases its numbers of parameters and training time. This paper proposes to recognize rotated targets of sonar images using a novel CNN with Rotated Inputs (RICNN), which doesn’t need data augmentation. During training, RICNN was trained with sonar images of targets only at one orientation, which avoid it to learn multiple rotated versions of the same targets, and reduces both number of parameters and training time of CNN. During testing, RICNN calculated classification scores for each test image and its all-possible rotated versions. The max of these classification scores were used to simultaneously estimate the category and orientation of each target. Besides, to improve the generalization of RICNN on imbalanced sonar datasets, this paper also designs an imbalanced data sampler. Experiments on a self-made small, imbalanced sonar image rotated target recognition dataset show that the improved RICNN achieves 4.25% higher classification accuracy than data augmentation, and reduces the number of parameters and training time to 2.25% and 19.2% of that of data augmentation method. Moreover, RICNN achieves comparable orientation estimation accuracy with a CNN orientation regressor trained with data augmentation. Codes, dataset are publicly available.