TL;DR
LE-RTDETR (Lightweight Edge-enhanced RT-DETR), a resource-efficient edge-enhanced detector for underwater objects, shows consistent detection performance across different underwater debris datasets while reducing model complexity and maintaining a favorable balance between detection accuracy and inference efficiency.
Results indicate that modeling underwater degradation and boundary uncertainty improves the robustness and reliability of marine debris detection in challenging underwater environments.
Underwater object detection faces severe challenges caused by light attenuation, scattering, spatially varying turbidity, and boundary blur, which weaken object-related visual signals and reduce localization reliability. This letter presents MED, a Mamba-Enhanced Detector for degradation-aware underwater object detecti...
The proposed UW-D-FINE, an enhanced real-time detector addressing underwater object detection challenges through three key innovations, enhances the backbone by integrating parallel multi-scale convolutional branches with omnidirectional depthwise convolutions, enabling more effective extraction of discriminative featu...
A novel underwater target detection framework that integrates feature enhancement with semantic-spatial guided fusion, built upon the RT-DETR architecture, that significantly reduces false positives and missed detections while maintaining real-time performance is proposed.
Underwater object detection plays a crucial role in fisheries resource assessment and ecological environment protection. Current underwater object detection models are characterized by large parameter sizes and high computational costs, which hinder the simultaneous achievement of lightweight deployment and high detect...
TRIDEN-YOLO, a lightweight detector built upon YOLOv11n, provides the primary reparameterized contextual representation design through multi-branch training and inference-time fusion, while HFFE and GCD loss are incorporated to enhance hierarchical feature fusion and boundary-aware localization.
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