# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
###### Abstract  
Automatic object detection by satellite remote sensing images is of great significance for resource exploration and natural disaster assessment. To solve existing problems in remote sensing image detection, this article proposes an improved YOLOX model for satellite remote sensing image automatic detection. This model is named RS-YOLOX. To strengthen the feature learning ability of the network, we used Efficient Channel Attention (ECA) in the backbone network of YOLOX and combined the Adaptively Spatial Feature Fusion (ASFF) with the neck network of YOLOX. To balance the numbers of positive and negative samples in training, we used the Varifocal Loss function. Finally, to obtain a high-performance remote sensing object detector, we combined the trained model with an open-source framework called Slicing Aided Hyper Inference (SAHI). This work evaluated models on three aerial remote sensing datasets (DOTA-v1.5, TGRS-HRRSD, and RSOD). Our comparative experiments demonstrate that our model has the highest accuracy in detecting objects in remote sensing image datasets. object detection; remote sensing image; attention mechanisms; feature fusion; varifocal loss; Slicing Aided Hyper Inference (SAHI) +
Footnote †: journal: Medical Image Analysis  
In recent years, with the application and development of deep learning in the field of computer vision, many excellent object detection models based on deep learning methods have been proposed, for example, the two-stage model represented by the R-CNN series [10; 11; 12], and the one-stage model represented by SSD [13; 14] and YOLO [15; 16; 17; 18; 19; 20]. These models have achieved impressive results in the detection of natural images. Researchers have introduced advanced detection algorithms into the application of remote sensing images, the goal being to overcome the difficulties in satellite remote sensing image detection. [PERSON] et al. [21] used an improved Faster R-CNN model to detect tailing ponds in remote sensing images. [PERSON] et al. [22] used an unsupervised model to detect fire-prone areas. [PERSON] [23] proposed the YOLT model, which is an improved YOLOv2 model for satellite remote sensing image detection. Although these methods have achieved good results, they only improve one direction or detection of one category in remote sensing images and do not systematically solve the problems of remote sensing image detection. Focusing on the problems of detecting objects in satellite remote sensing images, this article proposes a high-precision automatic detection method for satellite remote sensing images: RS-YOLOX. We obtained this model by improving the YOLOX model. We evaluated our proposed methods by using three aerial remote sensing datasets (DOTA-v1.5, TGRS-HRRSD, and RSOD). Our model achieved good results on these three datasets, outperforming all YOLO models in accuracy. The novelty of this article is exemplified by the following:  
* We propose a state-of-the-art YOLO model that surpasses all previous YOLO models in detection accuracy in satellite remote sensing images. * We use Efficient Channel Attention (ECA) and the Adaptively Spatial Feature Fusion (ASFF) in YOLOX to improve the model's accuracy in detecting small objects in remote sensing images. * We use a new loss function, Varifocal Loss function (VFL), which alleviates the problem of the unbalanced training of positive and negative samples in model training. * We combine the detection model with the Slicing Aided Hyper Inference framework (SAHI) to realize the direct detection of large-size remote sensing images. The rest of this study is organized as follows: Section 2 introduces the current popular object detection methods and their application in remote sensing image detection and then introduces the YOLOX model that inspired this article. Section 3 focuses on our methods to improve YOLOX to address the problems of satellite remote sensing image detection. Section 4 presents the experimental results of our methods and our analysis of the results.