CUDU-Net: Collaborative up-sampling decoder U-Net for leaf vein segmentation

Abstract

Leaf vein is a common visual pattern in nature which provides potential clues for species identification, health evaluation, and variety selection of plants. However, as a critical step in leaf vein pattern analysis, segmenting vein from leaf image remains unaddressed due to its hierarchical curvilinear structure and busy background. In this study, we for the first time design a deep model which is tailored to address the segmentation of overall leaf vein structure. The proposed deep model, termed Collaborative Up-sampling Decoder U-Net (CUDU-Net), is an improved U-Net structure consisting of a fine-tuned ResNet extractor and a collaborative up-sampling decoder. The ResNet extractor utilizes residual module to explore high-dimensional features that are representative and abstract in the hidden layers of the network. The core of CUDU-Net is the collaborative up-sampling decoder which utilizes the complementarity of the bilinear-interpolation and deconvolution, to enhance the decoding capability of the model. The bilinear-interpolation can recovery key veins while the deconvolution actively learns to supplement more fine-grained features of the tertiary veins. In addition, we embed the strip pooling in the skip-connection to distill the vein-related semantics for performance boosting. Two leaf vein segmentation datasets, termed SoyVein500 and CottVein20, are built for model validation and generalization ability test. The extensive experimental results show that our proposed CUDU-Net outperforms the state-of-the-art methods in both segmentation accuracy and generalization ability.