【摘 要】
：
光学遥感图像受背景复杂度和语义信息量大等影响,在检测精度和效率上仍然存在一定的不足。本文提出了以Resnet50为基础特征提取的SSD300网络模型,加入注意力机制CBAM模块和特征融合FPN模块,采用Soft-NMS策略选取最终预测框,对遥感图像飞机目标进行更加有效地检测。最后,在2 150张飞机遥感图像数据集上进行训练,当IoU为0.5和0.75时平均精度MAP达到92.54%和63.44%,
【机 构】
：
宁夏大学 物理与电子电气工程学院,宁夏 银川 750021;宁夏大学 物理与电子电气工程学院,宁夏 银川 750021;宁夏沙漠信息智能感知重点实验室,宁夏 银川 750021
论文部分内容阅读
光学遥感图像受背景复杂度和语义信息量大等影响,在检测精度和效率上仍然存在一定的不足。本文提出了以Resnet50为基础特征提取的SSD300网络模型,加入注意力机制CBAM模块和特征融合FPN模块,采用Soft-NMS策略选取最终预测框,对遥感图像飞机目标进行更加有效地检测。最后,在2 150张飞机遥感图像数据集上进行训练,当IoU为0.5和0.75时平均精度MAP达到92.54%和63.44%,较改进前的算法模型提升了5.04%和11.38%,检测速度达到24.1FPS。实验结果表明,该方法可以有
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