###### Abstract  
In recent years, object detection in remote sensing images has become a popular topic in computer vision research. However, there are various problems in remote sensing object detection, such as complex scenes, small objects in large fields of view, and multi-scale object in different categories. To address these issues, we propose DFPN-YOLO, a dense feature pyramid network for remote sensing object detection. To address difficulties in detecting small objects in large scenes, we add a larger detection layer on top of the three detection layers of YOLOv3, and we propose Dense-FPN, a dense feature pyramid network structure that enables all four detection layers to combine semantic information before sampling and after sampling to improve the performance of object detection at different scales. In addition, we add an attention module in the residual blocks of the backbone to allow the network to quickly extract key feature information in complex scenes. The results show that the mean average precision (mAP) of our method on the RSOD datasets reached 92%, which is 8% higher than the mAP of YOLOv3, and the mAP increased from 62.41% on YOLOv3 to 69.33% with our method on the DIOR datasets, outperforming even YOLOv4. 202212499720  
technology and deep learning, researchers have applied convolutional neural networks (CNNs) [15] to remote sensing object detection and achieved good results. [PERSON] et al. proposed YOLOv3, an incremental improvement [16] over previous detection methods. [PERSON] et al. proposed dense attention pyramid networks for multiscale ship detection in SAR images [17]. [PERSON] et al. proposed CF2 PN [18], a cross-scale feature fusion pyramid network-based method for remote sensing object detection. [PERSON] et al. proposed FE-YOLO [19], a feature-enhancement network for remote sensing object detection. Compared with traditional object detection algorithms, object detection algorithms based on CNNs are more accurate, allowing them to detect multiscale objects and small objects in remote sensing images with high accuracy. CNNs can extract spatial context information and have been widely used to detect objects in remote sensing images. At present, the most common neural networks for object detection are neural networks based on region proposal and neural networks based on anchor box regression. Most region proposal-based neural networks are two-stage networks that first determine the approximate object location based on the region proposal network and then accurately predict the object class and regress to the exact bounding box. While this step-by-step learning strategy improves the detection accuracy of these networks, it also increases the detection time and the difficulty in achieving efficient processing, and the training time is too long for remote sensing images with large input image sizes. Some typical examples of such networks include R-CNN [20], Fast R-CNN [21], and Faster R-CNN [22]. Most neural networks based on anchor box regression are one-stage networks that treat the whole prediction process as a regression process. This simplification of the process not only maintains the accuracy but also increases the speed; examples of such networks include the SSD [23; 24; 25], YOLO [26; 27; 28] series, and Efficientdet [29; 30]. Among them, YOLO series networks are typical neural networks based on anchor box regression, and several versions, such as YOLOv2 [31], YOLOv3 [16], YOLOv4 [32], and YOLOv5 [33], have been open-sourced. Among these versions, YOLOv3, YOLOv4, and YOLOv5 achieve a good balance between speed and accuracy when faced with the demands of traditional object detection applications, and they can achieve both efficient processing and good performance. However, when these methods are applied directly to remote sensing image detection, there are various problems, such as a lower detection accuracy for objects with large-scale differences and difficulty detecting small objects in complex scenes.