UEAFM-YOLO: an edge-aware and multi-scale feature learning architecture for intelligent underwater visual data analysis

Abstract

Underwater object detection (UOD) is a major process in marine exploration, ecological monitoring, autonomous underwater vehicles (AUVs) and ocean observation systems. However, underwater environments are affected by low contrast, color attenuation, light and blurred object boundaries, which degrade the performance of conventional UOD models. Practical underwater applications need lightweight architectures capable of deployment on resource limited platforms. For addressing these problems, this work suggests UEAFM-YOLO, a lightweight UOD model developed on the YOLOv11n network. First, an Underwater Edge-Aware Feature Module (UEAFM) module is introduced to enhance edge-aware representations and improve feature extraction under visually degraded underwater conditions. Second, a Cross-Stage Partial with Lightweight Star Fusion (C3k2LSF) is presented for reducing computational complexity and preserving feature representation capability. Third, an Underwater Multi-Scale Semantic Feature Pyramid Network (UMSFPN) is presented for strengthening semantic consistency and cross-scale feature interaction, which makes robust UOD. The proposed model integrates low-level structural features with high-level semantic features for improving object localization and classification performance. Experimental analysis demonstrates that UEAFM-YOLO achieves better mAP@0.5 values of 91.0% on DUO and 90.5% on UTDAC2020 datasets. Thus, the proposed model is highly suitable for marine surveillance, underwater robotics, and unmanned survey systems.