Remote sensing aircraft object detection algorithm based on attention mechanism

Abstract

In order to solve the problems of low algorithmic recall and severely missed detection in the object detection process of high-resolution and large-field-of-view remote sensing aircraft images, this paper designs a Remote sensing rotation detector (R2ODet). Firstly, this paper introduces the parameter calibration of oriented RPN and FRM to design a multiscale step-by-step detection refining decoder (DRD) to replace the R-CNN so that R2ODet can detect rotated aircraft targets and, at the same time, improves the algorithm's detection accuracy for tiny aircraft targets. Secondly, the global feature extraction module Attention Module-ResNet (AM-ResNet) is designed in this paper, which significantly improves the detection accuracy of the model. The experimental results show that the R2ODet designed in this paper improves by 4.54% and 0.55% compared with the mAP0.5 of R3Det and G-Rep, which can be applied in remote sensing aircraft object detection.