Vehicle detection is an important method for understanding high-resolution remote sensing images. Deep convolutional neural network (DCNN)-based methods have improved many computer vision tasks and have achieved state-of-the-art results in many object detection datasets. Object detection of remote sensing images has been radically changed by the introduction of DCNN. Considering correlation between the scale distribution of objects and spatial resolution of remote sensing images, we propose an improved vehicle detection method based on a YOLOv3 model. A multi-scale clustering anchor box generation algorithm is proposed to obtain the anchor box parameters that match the resolution of each layer of the feature pyramid of model. This allows us to get more accurate anchor parameters. Focal loss is introduced into the default loss function to reduce the weight of negative samples, which were easily classified, that focus the model training process on samples that are difficult to classify. For the imbalance problem of positive and negative samples in the detection method based on the prior anchor box, focal loss is used to focus the model training process on samples that are difficult to classify. The experiment is performed on a dataset consisting of remote sensing images obtained from Worldview-3, and the results show that compared with the basic YOLOv3 algorithm, the average accuracy of vehicle detection is improved by 8.44%. The accuracy of vehicle detection of high-resolution remote sensing images is significantly improved while maintaining the speed of single-stage target detection. This approach is tested on an xView dataset consisting of remote sensing images obtained from Worldview-3. In addition, through using the proposed method, the average precision of vehicle detection increased by 8.44%. The experimental results show that the proposed method can be used for object detection in high-resolution remote sensing images effectively, and this method can significantly improve the performance of the model without sacrificing inference speed.

中文翻译：

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用于高分辨率遥感影像中车辆检测的改进YOLOv3模型

车辆检测是理​​解高分辨率遥感影像的重要方法。基于深度卷积神经网络（DCNN）的方法改进了许多计算机视觉任务，并在许多对象检测数据集中获得了最新技术成果。引入DCNN彻底改变了遥感图像的目标检测。考虑到物体的尺度分布与遥感图像的空间分辨率之间的相关性，我们提出了一种基于YOLOv3模型的改进的车辆检测方法。提出了一种多尺度聚类锚盒生成算法，以获取与模型特征金字塔各层分辨率相匹配的锚盒参数。这使我们可以获得更准确的锚参数。将焦点损失引入默认损失函数中以减少易于分类的负样本的权重，这些负样本使模型训练过程集中于难以分类的样本。针对基于先验锚框的检测方法中正负样本的不平衡问题，使用焦点损失将模型训练过程集中在难以分类的样本上。实验是在一个包含从Worldview-3获得的遥感图像的数据集上进行的，结果表明，与基本的YOLOv3算法相比，车辆检测的平均准确性提高了8.44％。在保持单级目标检测速度的同时，大幅提高了车辆对高分辨率遥感影像的检测精度。该方法在xView数据集上进行了测试，该数据集包含从Worldview-3获得的遥感图像。此外，通过使用所提出的方法，车辆检测的平均精度提高了8.44％。实验结果表明，该方法可以有效地用于高分辨率遥感影像的目标检测，在不影响推理速度的前提下，可以显着提高模型的性能。