Oriented object detection based on cross-scale information fusion

Chen Li, Tongzhou Zhao, Chengbo Mao, Wei Hu
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引用次数: 1

Abstract

Due to the enormous size discrepancies between classes and within classes, as well as the high degree of resemblance across classes of oriented objects, traditional remote sensing object detection was made difficult. Although coping with huge scale differences and high inter-class similarity was made possible by multi-scale information fusion, the multiscale weight fusion technique neglected the impact of cross-scale on picture semantic feature extraction, leading to subpar detection performance. The performance of the delayed inference was caused by the rotating region proposal network, which produced high-quality ideas while expanding the network’s capacity. In this study, a cross-scale shift oriented object detection method was suggested. First, by creating a feature pyramid network, the multi-layer feature maps were successfully fused. First, the multi-layer feature maps were effectively fused by reconstructing a feature pyramid network. A cross-scale shift module was simultaneously introduced to FPN to enhance the correlation between multi-scale properties. Finally, to raise the quality of the bounding boxes produced, an oriented region proposal network (ORPN) was used. On remote sensing datasets from DOTA-V1.5, the proposed method fared better than the control group in terms of detection accuracy.
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基于跨尺度信息融合的定向目标检测
由于类与类之间和类内的巨大尺寸差异,以及类与类之间的高度相似,使得传统的遥感目标检测变得困难。虽然多尺度信息融合可以处理巨大的尺度差异和高类间相似性,但多尺度权重融合技术忽略了跨尺度对图像语义特征提取的影响,导致检测性能欠佳。延迟推理的性能是由旋转区域提议网络引起的,它在扩展网络容量的同时产生了高质量的想法。本研究提出了一种面向跨尺度位移的目标检测方法。首先,通过构建特征金字塔网络,成功融合了多层特征映射;首先,通过重构特征金字塔网络实现多层特征映射的有效融合;同时在FPN中引入了跨尺度移位模块,增强了多尺度特性之间的相关性。最后,为了提高生成的边界框的质量,采用了面向区域建议网络(ORPN)。在DOTA-V1.5遥感数据集上,该方法的检测精度优于对照组。
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