An Optimized Class Incremental Learning Network with Dynamic Backbone Based on Sonar Images

Author:

Chen Xinzhe12,Liang Hong12

Affiliation:

1. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

2. Ocean Institute of Northwestern Polytechnical University, Taicang 215400, China

Abstract

Class incremental learning with sonar images introduces a new dimension to underwater target recognition. Directly applying networks designed for optical images to our constructed sonar image dataset (SonarImage20) results in significant catastrophic forgetting. To address this problem, our study carefully selects the Dynamically Expandable Representation (DER)—recognized for its superior performance—as the baseline. We combine the intrinsic properties of sonar images with deep learning theories and optimize both the backbone and the class incremental training strategies of DER. The culmination of this optimization is the introduction of DER-Sonar, a class incremental learning network tailored for sonar images. Evaluations on SonarImage20 underscore the power of DER-Sonar. It outperforms competing class incremental learning networks with an impressive average recognition accuracy of 96.30%, a significant improvement of 7.43% over the baseline.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Ocean Engineering,Water Science and Technology,Civil and Structural Engineering

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