A two-stage approach for ship detection in restricted visibility based on dehazing and SE-YOLO algorithms

The rapid and automatic detection of ships in restricted visibility is crucial for protecting the safety of maritime navigation. The frequent presence of fog in maritime environments causes restricted visibility, contributing to a higher occurrence of maritime accidents. Aiming to address ship target detection's low accuracy in restricted visibility weather, this paper proposes an improved dehazing model based on a two-stage ship detection algorithm. First, during the dehazing stage, a specialized model for maritime environments addresses the problem of unclear ship features in foggy images. The model is trained by a combination of synthetic images training and real images fine-tuning. Then, during the detection stage, a modified YOLO network for small ship detection called SE-YOLO is introduced to solve the issue of small ship targets' low detection accuracy. On the one hand, the modified Spatial Pyramid Pooling – Fast(SPPF) module is designed to reduce information loss during feature extraction; on the other hand, the attention mechanisms are integrated to enhance the network's sensitivity to the details of small ship targets and improve the model's overall detection performance for ships. Moreover, to simulate the real sea scene and test the effectiveness of our method, a maritime-haze dataset containing different concentrations of fog and various brightness is made for this research. Finally, The experimental results indicate that, compared to the traditional YOLOv5 method, our method performs better on detecting ships in restricted visibility environments, with the mean average precision (mAP) increased from 62.64% to 77.23%.

在低能见度条件下快速自动检测船舶，对保障海上航行安全至关重要。海洋环境中雾的频繁出现会导致能见度受限，进而提高海上事故的发生率。针对低能见度气象条件下船舶目标检测精度偏低的问题，本文提出了一种基于两阶段船舶检测算法的改进去雾模型。首先，在去雾阶段，针对雾天图像中船舶特征模糊的问题，设计了适配海洋环境的专用模型；该模型通过合成图像训练与真实图像微调相结合的方式完成训练。其次，在检测阶段，提出了一种用于小型船舶检测的改进型YOLO网络SE-YOLO，以解决小型船舶目标检测精度不足的问题。一方面，设计了改进的快速空间金字塔池化（Spatial Pyramid Pooling – Fast, SPPF）模块，以降低特征提取过程中的信息损失；另一方面，融入注意力机制，以提升网络对小型船舶目标细节的敏感度，改善模型整体的船舶检测性能。此外，为模拟真实海上场景并验证所提方法的有效性，本研究构建了包含不同浓度雾霭与多种亮度条件的海洋雾天数据集。最终实验结果表明，相较于传统YOLOv5方法，本文所提方法在低能见度环境下的船舶检测任务中表现更优，平均精度均值（mean average precision, mAP）从62.64%提升至77.23%。

搜集汇总

背景与挑战

背景概述

该数据集是为测试两阶段船舶检测算法（结合去雾和SE-YOLO算法）而创建的海上雾霾数据集，包含不同浓度雾气和各种亮度条件下的图像。实验表明，该方法在受限能见度环境下比传统YOLOv5方法的平均精度提高了14.59%。