基于FFC-SSD模型的光学遥感图像目标检测

Object Detection in Optical Remote Sensing Images Based on FFC-SSD Model

面向高效、高精度光学遥感图像目标检测应用,重点针对提升SSD(single shot multibox detector)模型对图像中聚集分布的小尺寸目标检测精度的难点,提出一种FFC-SSD(multi-scale feature fusion&clustering SSD)改进模型;设计目标框分组聚类(BGC)模块,采用分组聚类的方法获得更符合目标样本尺寸分布的默认目标框参数并给予小尺寸目标更多关注,以有效提升网络对目标位置信息的提取能力;设计反池化高效多尺度特征融合(MSFF)模块,以在增强模型目标特征提取能力的同时有效减小模型效率损耗。实验结果显示了所提模型对光学遥感图像目标检测的有效性与适用性,较好地实现了精度与效率的平衡,对小尺寸目标具有较高的检测精度。

For the applications of efficient high-precision object detection in optical remote sensing(RS)images,this paper focuses on the difficulty of improving the detection accuracy of the SSD(single shot multibox detector)model on small and densely distributed objects in such images.An improved model FFC-SSD(multi-scale feature fusion&clustering SSD)is thereby proposed.For this purpose,a bounding-box group clustering(BGC)module is designed.Group clustering is implemented to obtain default object frame parameters that are more consistent with the size distribution of object samples and gives more attention to small objects.This module effectively improves the network’s ability to extract object locations.Then,an efficient de-pooling multi-scale feature fusion(MSFF)module is designed to enhance the ability of the model to extract object features and effectively reduce the efficiency loss of the model at the same time.The experimental results demonstrate the effectiveness and applicability of the FFC-SSD model for object detection in optical remote sensing images.The proposed model achieves a favorable balance between precision and efficiency and has high detection accuracy on small objects.