Improving Underwater Acoustic Classification Through Learnable Gabor Filter Convolution and Attention Mechanisms
PositiveArtificial Intelligence
- A new study has introduced GSE ResNeXt, a deep learning architecture that enhances underwater acoustic target classification by integrating learnable Gabor convolutional layers with a ResNeXt backbone and squeeze-and-excitation attention mechanisms. This innovation addresses the challenges posed by complex underwater noise and limited datasets, improving the model's ability to extract discriminative features.
- This development is significant as it aims to improve the accuracy and robustness of underwater acoustic classification, which is essential for environmental monitoring and defense applications. The integration of advanced machine learning techniques could lead to more reliable detection and classification of underwater targets.
- The introduction of GSE ResNeXt reflects a broader trend in artificial intelligence where deep learning architectures are increasingly being tailored to specific challenges across various domains. Similar advancements in medical imaging and other fields highlight the ongoing efforts to enhance classification systems, demonstrating the potential for machine learning to address complex real-world problems.
— via World Pulse Now AI Editorial System
