基于anchor-free的光学遥感舰船关重部位检测算法

Ship’s critical part detection algorithm based on anchor-free in optical remote sensing

针对基于深度学习的遥感舰船检测算法存在精细化程度不足、检测效率低的问题,提出一种基于anchor-free的光学遥感舰船关重部位检测算法。所提算法以全卷积的单阶段目标检测(FCOS)算法为基准,在主干网络中引入全局上下文模块,提高网络的特征表达能力;为更好地描述目标的方向性,在预测阶段构建了具有方向表征能力的回归分支;对中心度函数进行优化,使其具备方向感知和自适应能力。实验结果表明:在自建舰船关重部位数据集和HRSC2016上,所提算法的平均精度(AP)比FCOS算法有显著提升;与其他算法相比,所提算法在检测速度和检测精度上均表现优越,具有较高的检测效率。

Low detection effectiveness and inadequate refinement plague the existing deep learning-based remote sensing ship detection technique.To address the above problems,an optical remote sensing ship critical part detection algorithm based on anchor-free is proposed.The proposed algorithm takes fully convolutional one-stage object detection(FCOS)as the benchmark algorithm and introduces a global context module in the backbone network to improve the feature representation capability of the network.In the prediction step,a regression branch with orientation representation capabilities is built to more accurately describe the orientation of targets.The centrality function is optimized to make it direction-aware and adaptive.The experimental results show that the average precision(AP)of the proposed algorithm is significant improved over FCOS algorithm on the self-built ship critical part dataset and HRSC2016,respectively.Compared with other algorithms,the proposed algorithm has superior performance in both detection speed and detection accuracy and has high detection efficiency.