Toward Structural Learning and Enhanced YOLOv4 Network for Object Detection in Optical Remote Sensing Images,Advanced Theory and Simulations

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Toward Structural Learning and Enhanced YOLOv4 Network for Object Detection in Optical Remote Sensing Images
Advanced Theory and Simulations ( IF 2.9 ) Pub Date : 2022-03-09 , DOI: 10.1002/adts.202200002
Kun Wang ₁ , Maozhen Liu ₁

Affiliation

With the maturity of technological tools such as satellites and airplanes, object detection in optical remote sensing images have been widely used in military and civilian fields. Due to the interference of extensive multi-scale targets and complex backgrounds, the existing algorithms are still limited, especially for small-scale targets. To solve this problem, a high-precision remote sensing detection method based on the advanced YOLOv4 framework is proposed. First, a clustering algorithm that combines knowledge of object scales to generate a priori anchor boxes with a higher matching degree is proposed. The feature extension module is designed to expand the receptive field of the backbone network and capture important contextual information. After that, coordinate attention (CA) is introduced to suppress the extra noise under multi-scale fusion that is rarely noticed in previous work. In addition, a convolutional idea of self-learning structure has also been novelly proposed and combined with a high-resolution network to efficiently enhance the performance of the network under limited computing resources. Finally, detailed experiments are performed on the HRRSD dataset. The experimental results show that the network has a more satisfactory superiority, and it is conducive to the advancement of optical remote sensing technology.

中文翻译：

面向光学遥感图像中目标检测的结构学习和增强型 YOLOv4 网络

随着卫星、飞机等技术工具的成熟，光学遥感图像中的目标检测已广泛应用于军事和民用领域。由于广泛的多尺度目标和复杂背景的干扰，现有算法仍然有限，特别是对于小尺度目标。针对这一问题，提出了一种基于先进的YOLOv4框架的高精度遥感检测方法。首先，提出了一种结合对象尺度知识生成匹配度较高的先验锚框的聚类算法。特征扩展模块旨在扩展骨干网络的感受野并捕获重要的上下文信息。在那之后，引入坐标注意（CA）来抑制多尺度融合下的额外噪声，这在以前的工作中很少注意到。此外，还新颖地提出了一种自学习结构的卷积思想，并与高分辨率网络相结合，在有限的计算资源下有效地提升了网络的性能。最后，在 HRRSD 数据集上进行了详细的实验。实验结果表明，该网络具有较为令人满意的优越性，有利于光学遥感技术的进步。在 HRRSD 数据集上进行了详细的实验。实验结果表明，该网络具有较为令人满意的优越性，有利于光学遥感技术的进步。在 HRRSD 数据集上进行了详细的实验。实验结果表明，该网络具有较为令人满意的优越性，有利于光学遥感技术的进步。

更新日期：2022-03-09

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