论文标题
基于快速傅立叶卷积的遥控传感器图像对象检测地球观察
Fast Fourier Convolution Based Remote Sensor Image Object Detection for Earth Observation
论文作者
论文摘要
遥控传感器图像对象检测是地球观察的重要技术，用于各种任务，例如森林火灾监测和海洋监测。尽管有很大的发展，但图像对象检测技术尽管有很大的发展，但由于小对象的像素有限，仍在努力处理遥控传感器图像和小规模对象。现有的许多研究表明，促进小物体检测的有效方法是引入空间环境。同时，最近对图像分类的研究表明，光谱卷积操作可以比空间域更有效地感知频域中的长期空间依赖性。受到这一观察的启发，我们提出了用于遥感对象检测的频率感知特征金字塔框架（FFPF），该框架由新型的频率吸引重新NET（F-Resnet）和双侧光谱感知特征特征性金字塔网络（BS-FPN）组成。具体而言，提出了F-Resnet来感知光谱上下文信息，通过将频域卷积插入主链的每个阶段，从而提取小物体的富特征。据我们所知，这是第一项将频域卷积引入遥感对象检测任务的工作。此外，BSFPN旨在使用双边采样策略和跳过连接，以更好地对象在不同尺度上的对象特征的关联进行建模，以从F-Resnet中释放光谱上下文信息的潜力。进行了广泛的实验，以在光学遥感图像数据集（DIOR和DOTA）中进行对象检测。实验结果证明了我们方法的出色性能。它可以达到平均准确性（地图），没有任何技巧。
Remote sensor image object detection is an important technology for Earth observation, and is used in various tasks such as forest fire monitoring and ocean monitoring. Image object detection technology, despite the significant developments, is struggling to handle remote sensor images and small-scale objects, due to the limited pixels of small objects. Numerous existing studies have demonstrated that an effective way to promote small object detection is to introduce the spatial context. Meanwhile, recent researches for image classification have shown that spectral convolution operations can perceive long-term spatial dependence more efficiently in the frequency domain than spatial domain. Inspired by this observation, we propose a Frequency-aware Feature Pyramid Framework (FFPF) for remote sensing object detection, which consists of a novel Frequency-aware ResNet (F-ResNet) and a Bilateral Spectral-aware Feature Pyramid Network (BS-FPN). Specifically, the F-ResNet is proposed to perceive the spectral context information by plugging the frequency domain convolution into each stage of the backbone, extracting richer features of small objects. To the best of our knowledge, this is the first work to introduce frequency-domain convolution into remote sensing object detection task. In addition, the BSFPN is designed to use a bilateral sampling strategy and skipping connection to better model the association of object features at different scales, towards unleashing the potential of the spectral context information from F-ResNet. Extensive experiments are conducted for object detection in the optical remote sensing image dataset (DIOR and DOTA). The experimental results demonstrate the excellent performance of our method. It achieves an average accuracy (mAP) without any tricks.
