Multi-Swin Mask Transformer for Instance Segmentation of Agricultural Field Extraction

With the rapid development of digital intelligent agriculture, the accurate extraction of field information from remote sensing imagery to guide agricultural planning has become an important issue. In order to better extract fields, we analyze the scale characteristics of agricultural fields and incorporate the multi-scale idea into a Transformer. We subsequently propose an improved deep learning method named the Multi-Swin Mask Transformer (MSMTransformer), which is based on Mask2Former (an end-to-end instance segmentation framework). In order to prove the capability and effectiveness of our method, the iFLYTEK Challenge 2021 Cultivated Land Extraction competition dataset is used and the results are compared with Mask R-CNN, HTC, Mask2Former, etc. The experimental results show that the network has excellent performance, achieving a bbox_AP50 score of 0.749 and a segm_AP50 score of 0.758. Through comparative experiments, it is shown that the MSMTransformer network achieves the optimal values in all the COCO segmentation indexes, and can effectively alleviate the overlapping problem caused by the end-to-end instance segmentation network in dense scenes.

Automated summary

With the rapid development of digital intelligent agriculture, accurate extraction of field information from remote sensing imagery to guide agricultural planning has become important. In this study, we analyze the scale characteristics of agricultural fields and incorporate the multi-scale idea into a Transformer model called the Multi-Swin Mask Transformer (MSMTransformer). The experimental results using the iFLYTEK Challenge 2021 Cultivated Land Extraction competition dataset show that the MSMTransformer network achieves excellent performance, outperforming other methods in terms of COCO segmentation indexes and effectively addressing the overlapping problem in dense scenes.