ACFEM-RetinaNet Algorithm for Remote Sensing Image Target Detection

Aiming at the problem that RetinaNet is difficult to detect multi-scale targets and dense small targets in remote sensing target detection task, an ACFEM-RetinaNet remote sensing target detection algorithm is proposed. To solve the problem that the original backbone network extraction is not sufficient, the algorithm adopts Swin Transformer as the backbone network to improve the feature extraction ability of the algorithm and improve the detection accuracy. For the problem of dense small targets in remote sensing images, an adaptive context feature extraction module is proposed, which uses SK attention to guide deformable convolution with different dilation rates to adaptively adjust the receptive field and extract context features. Aiming at the problem of dense small targets in remote sensing images, the FreeAnchor module is introduced to design and optimize the anchor matching strategy from the perspective of a maximum likelihood estimation (MLE) procedure, so as to improve the detection accuracy. The experimental results show that the ACFEM-RetinaNet algorithm achieves 91.1% detection accuracy on the public remote sensing image target detection dataset RSOD, which is 4.6 percentage points higher than the original algorithm. The ACFEM-RetinaNet can be better applied to remote sensing image target detection. © 2016 Chinese Medical Journals Publishing House Co.Ltd. All rights reserved.