Multi-class geospatial object detection with remote sensing imagery has broad prospects in urban planning, natural disaster warning, industrial production, military surveillance and other applications. Accuracy and efficiency are two common measures for evaluating object detection models, and it is often difficult to achieve both at the same time. Developing a practical remote sensing object detection algorithm that balances the accuracy and efficiency is thus a big challenge in the Earth observation community. Here, we propose a comprehensive high-speed multi-class remote sensing object detection method. Firstly, we obtain a multi-volume YOLO (You Only Look Once) v4 model for balancing speed and accuracy, based on a pruning strategy of the convolutional neural network (CNN) and the one-stage object detection network YOLO v4. Moreover, we apply the Manhattan-Distance Intersection of Union (MIOU) loss function to the multi-volume YOLO v4 to further improve the accuracy without additional computational burden.

Secondly, mainly due to computing limitations, a remote sensing image that is large-size relative to a natural image must first be divided into multiple smaller tiles, which are then detected separately, and finally, the detection results are spliced back to match the original image. In the process of remote sensing image slicing, a large number of truncated objects appear at the edge of tiles, which will produce a large number of false results in the subsequent detection links. To solve this problem, we propose a Truncated Non-Maximum Suppression (NMS) algorithm to filter out repeated and false detection boxes from truncated targets in the spliced detection results. We compare the proposed algorithm with the state-of-the-art methods on the Dataset for Object deTection in Aerial images (DOTA) and DOTA v2. Quantitative evaluations show that $\mathit{mAP}$ and FPS reach 77.3 and 35 on DOTA, and 61.0 and 74 on DOTA v2. Overall, our method reaches the optimal balance between efficiency and accuracy, and realizes the high-speed remote sensing object detection.

遥感影像多类地理空间目标检测在城市规划、自然灾害预警、工业生产、军事监视等应用领域具有广阔的前景。准确度和效率是评价目标检测模型的两个常用指标，而两者往往难以同时实现。因此，开发一种兼顾精度和效率的实用遥感目标检测算法是地球观测界的一大挑战。在这里，我们提出了一种综合的高速多类遥感目标检测方法。首先，我们基于卷积神经网络 (CNN) 和单阶段目标检测网络 YOLO v4 的剪枝策略，获得了用于平衡速度和准确性的多体积 YOLO (You Only Look Once) v4 模型。而且，

其次，主要是由于计算的限制，相对于自然图像尺寸较大的遥感图像，必须先分割成多个较小的瓦片，然后分别检测，最后将检测结果拼接回与原始图像匹配。图片。在遥感图像切片过程中，瓦片边缘会出现大量截断的物体，这会在后续的检测环节产生大量的错误结果。为了解决这个问题，我们提出了一种截断非极大值抑制（NMS）算法，从拼接检测结果中的截断目标中过滤掉重复和错误的检测框。我们将提出的算法与航空图像中目标检测数据集 (DOTA) 和 DOTA v2 上的最新方法进行比较。定量评价表明$\mathit{地图}$FPS 在 DOTA 上达到 77.3 和 35，在 DOTA v2 上达到 61.0 和 74。总体而言，我们的方法在效率和准确性之间达到了最佳平衡，实现了高速遥感目标检测。