针对遥感图像目标检测中小目标物体漏检率高、检测精度低的问题，提出了一种检测精度更高的遥感图像小目标检测方法EfficientNet-YOLOv3。该方法基于YOLOv3算法，采用EfficientNet-B0网络替换原YOLOv3算法的骨干网络，能更有效地提取图像特征；增加预测分支以及优化先验框的大小和个数，提高对遥感图像小目标的检测效果；同时选择DIoU为损失函数，提高目标先验框回归的效率，改善漏检现象。DOTA遥感图像数据集上的实验结果表明，算法平均精度均值（mAP）为91.01%，比原YOLOv3平均精度均值（mAP）提高了11.82%，具有更高的检测精度。

In order to solve the problem of high omission ratio of small target objects in remote sensing image object detection, this paper puts forward a small target detection method EfficientNet-YOLOv3 for remote sensing images requiring higher detection accuracy. The method, based on YOLOv3, uses EfficientNet-B0 network to replace the backbone network of the original YOLOv3 algorithm, which can extract image features more effectively, increase the size and number of prediction branches and optimized prior boxes, and improve the detection effect of remote sensing small targets. At the same time, DIoU is selected as the loss function to improve the efficiency of target detection box regression and reduce the omission ratio. Experimental results on DOTA remote sensing image dataset show that the mean average precision mAP of the proposed algorithm is 91.01%, which is 11.82% higher than that of the original YOLOv3. Therefore, it has higher detection accuracy