Low altitude remote sensing instance segmentation algorithm based on enhanced Mask R CNN

DOI:	10.11809/bqzbgcxb2025.02.022
Keywords:	deep learning; image processing; remote sensing images; instance segmentation; improved mask r CNN algorithm; resnet 50
Abstract:	In response to the challenges of complex object detection and low precision in image segmentation in the remote sensing domain, an enhanced version of the Mask R CNN algorithm was proposed. The PMResNet 50 architecture was designed as the backbone network, with the Pyramid Squeeze Attention Modules facilitating information interaction between local and global channel attentions. Additionally, the Multilevel Feature Aggregation Module was employed to enhance the efficient aggregation of semantic information across input channels within PMResNet 50. A self calibrating convolutional module was introduced before RoI Align to expand the receptive field size of convolutional layers and perform calibration operations on bounding boxes and mask boxes. In the segmentation branch, the Mask Prediction Balanced Loss function was utilized to balance gradients of positive and negative samples for each class, achieving a smooth reduction in loss gradient handling. Upon testing on both our self built low altitude remote sensing dataset and the iSAID Reduce100 dataset, experimental results demonstrate that the improved algorithm achieves a 17.9% and 15.0% increase in box AP and mask AP, respectively, on our self built dataset. Similarly, on the iSAID Reduce100 dataset, the box AP and mask AP reach 49.62% and 50.27%, respectively. This algorithm effectively accomplishes detection and segmentation of remote sensing objects.
Issue:	Vol. 46 No. 2 (2025)
Published:	2025-02-28
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