Open AccessADFNet: accumulated decoder features for real‐time semantic segmentation
Author(s) -
Choi Hyunguk,
Ahn Hoyeon,
Kim Joonmo,
Jeon Moongu
Publication year - 2020
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2019.0289
Subject(s) - computer science , segmentation , encoder , artificial intelligence , rgb color model , computer vision , image segmentation , pattern recognition (psychology) , operating system
Semantic segmentation is one of the important technologies in autonomous driving, and ensuring its real‐time and high performance is of utmost importance for the safety of pedestrians and passengers. To improve its performance using deep neural networks that operate in real‐time, the authors propose a simple and efficient method called ADFNet using accumulated decoder features, ADFNet operates by only using the decoder information without skip connections between the encoder and decoder. They demonstrate that the performance of ADFNet is superior to that of the state‐of‐the‐art methods, including that of the baseline network on the cityscapes dataset. Further, they analyse the results obtained via ADFNet using class activation maps and RGB representations for image segmentation results.