RS-YOLOx: target feature enhancement and bounding box auxiliary regression based object detection approach for remote sensing

This work presents a method for remote sensing object detection (RSOD) based on target feature enhancement and bounding box (Bbox) auxiliary regression. Due to the characteristics of dense distribution, easy feature loss, and difficult Bbox regression (ground truth boxes of medium and small objects in remote sensing images usually only contain a few pixel sizes, making they difficult to regress from the global image), the problem of low accuracy of RSOD arises. Especially, it is easy to lose the features of medium and small objects in RSOD. This work proposes a target feature enhancement module, which enhances the feature transmission of medium and small objects by mapping deep and shallow features. Furthermore, considering that the existence of low-quality labeled data in remote sensing datasets will affect the training process of detection methods, this work proposes a wise intersection over union (Wise-IoU) loss. The Wise-IoU loss focuses on important ordinary-quality labels and improves the overall performance of RSOD. To solve the problem of difficult Bbox regression caused by the small size in remote sensing objects, this paper also proposes a coarse-to-fine Bbox regression model. The new model improves the regression speed and accuracy of medium and small object Bbox by using the auxiliary IoU loss. In addition, the validity and versatility of the method were verified on the Min-DOTA dataset, UCAS-AOD dataset, RSOD dataset, and Min-AI-TOD dataset. The results show that compared with other methods (see, e.g., FCOS, YOLOx, YOLOv7, and YOLOv8), our method has better detection performance and meets real-time detection requirements.