A lightweight transformer-enhanced YOLOv8 framework for robust underwater object detection and classification

HFQI-YOLO: a lightweight and efficient underwater object detection algorithm

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...

YEILAN-ConvViT: hybrid transformer-enhanced real-time marine object detection using improved YOLO with efficient inter-layer aggregation

An underwater object detection method integrating multi-dimensional attention and task-decoupling mechanism

A dynamic image feature fusion backbone module, termed DyC2F (dynamic lightweight convolution to fusion), is developed, which enhances the modeling of local textures and weak boundary features while maintaining low computational complexity.

An underwater garbage detection algorithm based on REG-YOLO

Underwater garbage detection plays a crucial role in maintaining the balance of aquatic ecosystems. To address the inefficiency and high cost of traditional manual debris retrieval, as well as the challenge of degraded detection accuracy in complex underwater environments, an innovative underwater waste detection algor...

YOLO-CBNet: A robust attention-enhanced detection framework for underwater fish recognition in aquaculture environments

The accurate identification of fish species remains a pivotal challenge in aquaculture, as it directly influences population management, health monitoring, and overall production efficiency. Yet, underwater environments often present difficult conditions such as reduced visibility, suspended particles, and uneven light...

TRIDEN-YOLO: a reparameterized interactive network for underwater object detection

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.