基于深度学习的复杂气象条件下海上船只检测

为了解决复杂海情环境下的不同种类和大小的舰船检测问题，提出一种实时的深度学习的目标检测算法。首先，提出了一种清晰图片和模糊图片（雨、雾等图片）判别的方法；然后，在YOLO v2的深度学习框架的基础上提出一种多尺度目标检测算法；最后，针对遥感图像舰船目标的特点，提出了一种改进的非极大值抑制和显著性分割算法，对最终的检测结果进一步优化。在复杂海情和气象条件下的舰船目标公开比赛的数据集上，实验结果表明，相比原始的YOLO v2，该方法的准确率提升了16%。

Ship detection under complex sea and weather conditions based on deep learning

In order to solve the detection of ships with different types and sizes under complex marine environment, a real-time object detection algorithm based on deep learning was proposed. Firstly, a discriminant method between sharp and fuzzy such as rainy and foggy images was proposed. Then a multi-scale object detection algorithm based on deep learning framework of You Only Look Once (YOLO) v2 was proposed. Finally, concerning the character of remote sensing images of ships, an improved non-maximum supression and saliency partitioning algorithm was proposed to optimize the final detection results. The experimental results show that, on the dataset of ship detection in an open competition under complex sea conditions and meteorological conditions, the precision of the proposed method is increased by 16% compared with original YOLO v2 algorithm.