Rotating-YOLO: A novel YOLO model for remote sensing rotating object detection

Abstract

Satellite remote sensing images are characterized by large rotation angles and dense targets, which result in less than satisfactory detection accuracy for existing remote sensing target detectors. To tackle these challenges, this paper introduces an object detection algorithm called Rotating-YOLO, which ensures the detection accuracy of remote sensing targets while also reducing the number of model parameters. Initially, an efficient multi-branch feature fusion (EMFF) is designed to filter out redundant feature information, thereby enhancing the model’s efficiency in feature extraction and fusion. Subsequently, to address the issue of sample imbalance in remote sensing images, this paper introduces angular parameters and adopts rotated bounding boxes to decrease the interference of background noise on the detection task. Additionally, the rotated bounding boxes are transformed into Gaussian distributions, and a new loss function named GaussianLoss is designed to calculate the loss between Gaussian distributions, assisting the model in better learning the size and orientation features of targets, thus improving detection accuracy. Finally, the efficient multi-scale attention (EMA) mechanism is embedded in the model’s neck in a residual form, and low-level feature extraction layers and corresponding detection heads are added to the backbone network to enhance the detection accuracy of small targets. Experimental results demonstrate that compared to the baseline model YOLOv8, the Rotating-YOLO model has reduced the number of parameters by 33.25% and increased the mean average precision (mAP) by 1.4%.