With the rapid development of optical remote sensing, it is urgent to find a reliable target detection method. Compared with traditional detection algorithms, a convolutional neural network has attracted considerable attention owing to its efficiency and high transitivity. However, different from general images, remote sensing images contain complex background information and dense small targets with changeable directions that make detection very challenging. To solve these problems and provide a stable and high-performance detection method, a rotated saliency fusion object detection (RSD) model based on “you only look once” (YOLO)v4 is established. First, salient image fusion technology is used to magnify target information. Second, the angle variable and rotated non-maximal suppression is introduced to improve the accuracy of rotated object detection by including the detection of dense objects. Third, the network structure is enhanced to improve the performance of small-target detection. Finally, the k-means algorithm and data enhancement are introduced to increase the robustness of the model. Extensive experiments demonstrate the superiority of the proposed model in detection speed and accuracy. The mean average precision of the proposed RSD model reaches 97.32% for the remote sensing images in a harbor area with an average detection speed of 13.41 s − 1.

中文翻译：

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基于任意定向卷积神经网络的港口区遥感目标检测

随着光学遥感技术的快速发展，迫切需要寻找一种可靠的目标检测方法。与传统检测算法相比，卷积神经网络因其高效和高传递性而备受关注。然而，不同于一般图像，遥感图像包含复杂的背景信息和密集的小目标，方向多变，使得检测非常具有挑战性。为了解决这些问题并提供稳定、高性能的检测方法，建立了基于“you only look once”（YOLO）v4的旋转显着性融合目标检测（RSD）模型。首先，利用显着图像融合技术放大目标信息。第二，引入角度变量和旋转非极大值抑制，通过包含密集物体的检测来提高旋转物体检测的精度。第三，增强网络结构，提高小目标检测性能。最后引入k-means算法和数据增强，增加模型的鲁棒性。大量实验证明了所提出的模型在检测速度和准确性方面的优越性。所提出的RSD模型的平均精度达到97.32%，对港区遥感图像平均检测速度为13.41 s − 1。引入k-means算法和数据增强，增加模型的鲁棒性。大量实验证明了所提出的模型在检测速度和准确性方面的优越性。所提出的RSD模型的平均精度达到97.32%，对港区遥感图像平均检测速度为13.41 s − 1。引入k-means算法和数据增强，增加模型的鲁棒性。大量实验证明了所提出的模型在检测速度和准确性方面的优越性。所提出的RSD模型的平均精度达到97.32%，对港区遥感图像平均检测速度为13.41 s − 1。