Aircraft Detection and Fine-Grained Recognition Based on High-Resolution Remote Sensing Images

Abstract

In order to realize the detection and recognition of specific types of an aircraft in remote sensing images, this paper proposes an algorithm called Fine-grained S2ANet (FS2ANet) based on the improved Single-shot Alignment Network (S2ANet) for remote sensing aircraft object detection and fine-grained recognition. Firstly, to address the imbalanced number of instances of various aircrafts in the dataset, we perform data augmentation on some remote sensing images using flip and color space transformation methods. Secondly, this paper selects ResNet101 as the backbone, combines space-to-depth (SPD) to improve the FPN structure, constructs the FPN-SPD module, and builds the aircraft fine feature focusing module (AF3M) in the detection head of the network, which reduces the loss of fine-grained information in the process of feature extraction, enhances the extraction capability of the network for fine aircraft features, and improves the detection accuracy of remote sensing micro aircraft objects. Finally, we use the SkewIoU based on Kalman filtering (KFIoU) as the algorithm’s regression loss function, improving the algorithm’s convergence speed and the object boxes’ regression accuracy. The experimental results of the detection and fine-grained recognition of 11 types of remote sensing aircraft objects such as Boeing 737, A321, and C919 using the FS2ANet algorithm show that the mAP0.5 of FS2ANet is 46.82%, which is 3.87% higher than S2ANet, and it can apply to the field of remote sensing aircraft object detection and fine-grained recognition.