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%.

受限能见度下船舶的快速自动检测对保障海上航行安全至关重要。海事环境中雾的频繁出现会导致能见度受限，进而推高海上事故的发生概率。针对受限能见度气象条件下船舶目标检测精度较低的问题，本文提出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，设计了适配海事环境的专用模型，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。随后，在检测阶段，提出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偏低的问题。一方面，设计改进的空间金字塔池化-快速版（Spatial Pyramid Pooling – Fast，SPPF）模块以减少特征提取过程中的信息损失；另一方面，集成注意力机制以提升网络对小型船舶目标细节的敏感度，进而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，本研究构建了涵盖不同雾度浓度与多种亮度条件的海事雾天数据集。最终实验结果表明，相较于传统YOLOv5方法，本文所提方法在受限能见度环境下的船舶检测性能更优，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

该数据集支持一项研究，提出了一种两阶段船舶检测方法，结合去雾技术和改进的YOLO网络（SE-YOLO），以提高在受限能见度环境下对小船舶目标的检测精度。研究包括一个专门的海上雾霾数据集，用于模拟真实海景并验证方法的有效性。