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Agriculture-Vision Challenge 2022 -- The Runner-Up Solution for Agricultural Pattern Recognition via Transformer-based Models
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The Agriculture-Vision Challenge in CVPR is one of the most famous and competitive challenges for global researchers to break the boundary between computer vision and agriculture sectors, aiming at agricultural pattern recognition from aerial images. In this paper, we propose our solution to the third Agriculture-Vision Challenge in CVPR 2022. We leverage a data pre-processing scheme and several Transformer-based models as well as data augmentation techniques to achieve a mIoU of 0.582, accomplishing the 2nd place in this challenge.
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Dual Atrous Separable Convolution for Improving Agricultural Semantic Segmentation
A modified DeepLabV3 with a dual atrous separable convolution module and a skip connection achieves 47.17 mIoU on Agriculture-Vision with 6.32 GFLOPs, outperforming its baseline and matching heavier transformer models.
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