論文（Papers）
Rose-Mamba-YOLO: an enhanced framework for efficient and accurate greenhouse rose monitoring A lightweight deep learning framework that enhances greenhouse rose monitoring by integrating Mamba-based feature modeling with YOLOv10 detection.
Featured on Frontiers, DOI Mamba-based super-resolution and semi-supervised YOLOv10 for freshwater mussel detection using acoustic video camera: A case study at Lake Izunuma, Japan A novel framework that integrates Mamba-based super-resolution with semi-supervised YOLOv10 to improve freshwater mussel detection in turbid underwater environments using acoustic video cameras.
Featured on ScienceDirect, DOI A Robust Detection Framework for Intelligent Growth Monitoring in Greenhouse Rose Cultivation Introduces ROSE-MAMBA-YOLO, a hybrid framework combining YOLOv11 with Mamba-inspired state-space modeling for robust rose detection in UAV imagery, achieving superior accuracy in challenging greenhouse environments.
Featured on IEEE Xplore, DOI Seafloor debris detection using underwater images and deep learning-driven image restoration: A case study from Koh Tao, Thailand An underwater monitoring framework that integrates super-resolution reconstruction with an optimized YOLOv8 detector (SFD-YOLO) to enhance image quality and achieve state-of-the-art accuracy in seafloor debris detection.
Featured on ScienceDirect, DOI Underwater Sea Cucumber Detection Using Consumer-Grade Amphibious UAV and Deep-Learning Based Computer Vision Presents a novel monitoring system that integrates a consumer-grade amphibious UAV with Mamba-based super-resolution and an enhanced YOLOv10 instance segmentation model to efficiently detect and map Holothurians in coral reef ecosystems.
Featured on IEEE Xplore, DOI Smart UAV-assisted rose growth monitoring with improved YOLOv10 and Mamba restoration techniques An integrated framework that combines Mamba-based super-resolution (MambaIR) with an improved YOLOv10 detector (ROSE-YOLO) to enhance UAV imagery for accurate and scalable greenhouse rose growth monitoring.
Featured on ScienceDirect, DOI YOLOv10 and Mamba-Based Super-Resolution for Smart Rose Growth Monitoring Using UAV Imagery A UAV-based monitoring framework that integrates Mamba-driven super-resolution with YOLOv10 detection to enhance imagery quality and achieve accurate growth assessment of greenhouse roses.
Featured on IEEE Xplore, DOI