为解决大多数舰船检测算法的精度不高、速度较慢等问题，提出一种显著性特征引导的舰船目标快速精细检测方法。首先，利用基于局部与全局整合的视觉显著模型定位目标区域，并通过区域提取得到候选目标切片；然后利用改进的均值聚类方法将目标切片分割为超像素集合；最后通过融合显著图和超像素分割结果，筛选属于目标的超像素来实现精细分割，得到舰船目标的候选区域。实验结果表明，该方法能够准确快速地定位舰船目标，且能精确刻画目标轮廓，更有利于后续舰船识别等后续工作的开展。

In order to solve the problem that majority of ship detection algorithms show the low accuracy and slow rate, a fine rapid ship detection algorithm based on salient feature guidance is proposed. First, the candidate target area is located through visual saliency model based on the local and global integration, and candidate target slices are gained through the region extraction. Then, the improved means clustering method is used to divide the target slice into super pixels. Finally, ship target regions are gained through filtering super pixels belonging to the target to achieve a fine segmentation after the integration of a significant figure and super-pixel segmentation. Experimental results show that the algorithm proposed in this paper can quickly and accurately locate the target ship, accurately depict the object contour, and is more conducive to subsequent follow-up work.