论文标题
EYNET：在遥感图像中进行机场检测的扩展YOLO
EYNet: Extended YOLO for Airport Detection in Remote Sensing Images
论文作者
论文摘要
如今，遥感图像中的机场侦查由于其在平民和军事范围中的战略作用而引起了极大的关注。尤其是，未开放和操作的飞机必须立即检测到安全区域以降落在紧急情况下。以前的计划遭受了各个方面的困扰，包括机场的复杂背景，规模和形状。同时，该方法的快速动作和准确性面临重大关注点。因此，这项研究提出了通过扩展Yolov3和Shearlet变换的有效方案。这样，Mobilenet和resnet18在类似的数据集上具有较少的层和参数再训练的层，被培训为基本网络。根据机场几何特性，在RESNET18的第一卷积层中考虑了具有不同尺度和方向的剪切滤波器作为视觉注意机制。此外，Yolov3中的主要扩展涉及具有新型结构的检测子网络，从而提高了对象表达能力和训练效率。此外，提出了新的增强和负面的采矿策略，以显着提高本地化阶段的绩效。 DIOR数据集上的实验结果表明，与传统的Yolov3和最先进的方案相比，该框架可靠地检测到各种区域中的不同类型的机场，并在复杂的场景中获得强大的结果。
Nowadays, airport detection in remote sensing images has attracted considerable attention due to its strategic role in civilian and military scopes. In particular, uncrewed and operated aerial vehicles must immediately detect safe areas to land in emergencies. The previous schemes suffered from various aspects, including complicated backgrounds, scales, and shapes of the airport. Meanwhile, the rapid action and accuracy of the method are confronted with significant concerns. Hence, this study proposes an effective scheme by extending YOLOV3 and ShearLet transform. In this way, MobileNet and ResNet18, with fewer layers and parameters retrained on a similar dataset, are parallelly trained as base networks. According to airport geometrical characteristics, the ShearLet filters with different scales and directions are considered in the first convolution layers of ResNet18 as a visual attention mechanism. Besides, the major extended in YOLOV3 concerns the detection Sub-Networks with novel structures which boost object expression ability and training efficiency. In addition, novel augmentation and negative mining strategies are presented to significantly increase the localization phase's performance. The experimental results on the DIOR dataset reveal that the framework reliably detects different types of airports in a varied area and acquires robust results in complex scenes compared to traditional YOLOV3 and state-of-the-art schemes.
