用YOLOv8和U-Net提升任意朝向船只检测精度
Ship Detection in Remote Sensing Imagery for Arbitrarily Oriented Object Detection
- 结合YOLOv8实时检测与U-Net实例分割，增强方向适应性
- YOLOv8达88% mAP，U-Net达89% mAP，显著提升边界精度
- 适合海事监控、灾害响应等需高精度船只识别场景
本文针对海上监视与生态监测等应用，提出一种新型船只检测系统。研究采用YOLOv8与改造版U-Net两个先进深度学习模型，显著提升船只检测准确率。评估指标包括平均精度均值（mAP）、处理速度与整体准确率。实验基于包含多样遥感图像的Airbus Ship Detection数据集，验证模型在不同朝向与环境下的泛化能力。传统方法在任意朝向、复杂背景与遮挡情况下表现不佳。本方案利用YOLOv8实现实时检测，U-Net用于船只实例分割。结果表明，YOLOv8在mAP上达到88%，具备高精度与快速响应能力；经适配的U-Net在实例分割任务中达到89% mAP，有效改善边界定位并应对遮挡问题。该研究推动了海事监控、应急响应与生态监测的技术进步，展示了深度学习在船只检测中的巨大潜力。
原文摘要 · Abstract (English)
This research paper presents an innovative ship detection system tailored for applications like maritime surveillance and ecological monitoring. The study employs YOLOv8 and repurposed U-Net, two advanced deep learning models, to significantly enhance ship detection accuracy. Evaluation metrics include Mean Average Precision (mAP), processing speed, and overall accuracy. The research utilizes the "Airbus Ship Detection" dataset, featuring diverse remote sensing images, to assess the models' versatility in detecting ships with varying orientations and environmental contexts. Conventional ship detection faces challenges with arbitrary orientations, complex backgrounds, and obscured perspectives. Our approach incorporates YOLOv8 for real-time processing and U-Net for ship instance segmentation. Evaluation focuses on mAP, processing speed, and overall accuracy. The dataset is chosen for its diverse images, making it an ideal benchmark. Results demonstrate significant progress in ship detection. YOLOv8 achieves an 88% mAP, excelling in accurate and rapid ship detection. U Net, adapted for ship instance segmentation, attains an 89% mAP, improving boundary delineation and handling occlusions. This research enhances maritime surveillance, disaster response, and ecological monitoring, exemplifying the potential of deep learning models in ship detection.
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