Open AccessDual Parallel Reverse Attention Edge Network : DPRA-EdgeNet
Author(s) -
Debayan Bhattacharya,
Christian Betz,
Dennis Eggert,
Alexander Schlaefer
Publication year - 2021
Publication title -
nordic machine intelligence
Language(s) - English
Resource type - Journals
ISSN - 2703-9196
DOI - 10.5617/nmi.9116
Subject(s) - segmentation , computer science , block (permutation group theory) , enhanced data rates for gsm evolution , artificial intelligence , object (grammar) , dual (grammatical number) , pattern recognition (psychology) , computer vision , series (stratigraphy) , algorithm , mathematics , geometry , art , literature , paleontology , biology
In this paper, we propose Dual Parallel Reverse Attention Edge Network (DPRA-EdgeNet), an architecture that jointly learns to segment an object and its edge. Specifically, the model uses two cascaded partial decoders to form two initial estimates of the object segmentation map and its corresponding edge map. This is followed by a series of object decoders and edge decoders which work in conjunction with dual parallel reverse attention modules. The dual parallel reverse attention (DPRA) modules repeatedly prunes the features at multiple scales to put emphasis on the object segmentation and the edge segmentation respectively. Furthermore, we propose a novel decoder block that uses spatial and channel attention to combine features from the preceding decoder block and reverse attention (RA) modules for object and edge segmentation. We compare our model against popular segmentation models such as U-Net, SegNet and PraNet and demonstrate through a five fold cross validation experiment that our model improves the segmentation accuracy significantly on the Kvasir-SEG dataset and Kvasir-Instrument dataset.