Transformer-Based Object Detection in Low-Altitude Maritime UAV Remote Sensing Images

Object detection technology plays an essential role in uncrewed aerial vehicle (UAV) sea search and rescue missions, which aim to quickly locate crucial targets such as trapped people and ships at sea in the complex marine environment. However, due to the restricted view angle of the UAV and the specificity of the working environment, the remote sensing images captured in the marine environment are characterized by small targets and considerable interference on the sea surface, which brings significant challenges to the UAV sea rescue mission. To address this issue, we propose an object detection model based on the Transformer architecture in this article. The model takes feature correction and double sampling detection transformer (FCDS-DETR) as the baseline and introduces a 2-D Gaussian probability density function as an additional attention mechanism to speed up the model’s location of suspicious targets in the image to improve the detection accuracy of the model for targets. At the same time, the denoising training method is introduced in the model’s training process to stabilize the bipartite graph matching and promote the convergence of the model. On the SDS ODv2 and AFO object detection datasets, our model achieves an average precision (AP) of 51% and 54.5%, respectively, which is an improvement of 5.7% and 4.4% compared to the baseline model’s performance on these datasets.