Energy-Efficient Design of Seabed Substrate Detection Model Leveraging CNN-SVM Architecture and Sonar Data

Wang, Keming; Wang, Chengli; Jin, Wenbing; Qi, Liuming

Energy-Efficient Design of Seabed Substrate Detection Model Leveraging CNN-SVM Architecture and Sonar Data

Wang, Keming and Wang, Chengli and Jin, Wenbing and Qi, Liuming (2024) Energy-Efficient Design of Seabed Substrate Detection Model Leveraging CNN-SVM Architecture and Sonar Data. EAI Endorsed Transactions on Energy Web.

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Abstract

This study introduces an innovative seabed substrate detection model that harnesses the complementary strengths of Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) to analyze sonar data with a focus on energy efficiency. The model addresses the challenges of underwater sensing

Item Type:	Article
Date Deposited:	04 Mar 2026 18:13
Last Modified:	11 Apr 2026 00:21
URI:	http://eprints.eai.eu/id/eprint/51628

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