ICNet for Real-Time Semantic Segmentation on High-Resolution Images

Abstract

We focus on the challenging task of real-time semantic segmentation in this paper. It finds many practical applications and yet is with fundamental difficulty of reducing a large portion of computation for pixel-wise label inference. We propose an image cascade network (ICNet) that incorporates multi-resolution branches under proper label guidance to address this challenge. We provide in-depth analysis of our framework and introduce the cascade feature fusion unit to quickly achieve high-quality segmentation. Our system yields real-time inference on a single GPU card with decent quality results evaluated on challenging datasets like Cityscapes, CamVid and COCO-Stuff.

Keywords

Computer scienceSegmentationInferenceFocus (optics)Artificial intelligenceFeature (linguistics)CascadePixelComputationTask (project management)Computer visionImage segmentationPattern recognition (psychology)Algorithm

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Publication Info

Year: 2018
Type: book-chapter
Pages: 418-434
Citations: 1512
Access: Closed

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APA Style

                            
                                    Hengshuang Zhao, 
                                
                                    Xiaojuan Qi, 
                                
                                    Xiaoyong Shen
                                
                                et al.
                            
                            (2018). 
                            ICNet for Real-Time Semantic Segmentation on High-Resolution Images. 
                            Lecture notes in computer science
                            
                            , 418-434.
                            https://doi.org/10.1007/978-3-030-01219-9_25

Identifiers

DOI: 10.1007/978-3-030-01219-9_25
PMID: 41323248
PMCID: PMC12662949
arXiv: 1704.08545

Data Quality

Data completeness: 79%