Importance-Aware Semantic Segmentation for Autonomous Driving System

By Bike Chen, Chen Gong, Jian Yang. 2017

41 citations recorded by the source corpus.

Semantic Segmentation (SS) partitions an image into several coherent semantically meaningful parts, and classifies each part into one of the predetermined classes. In this paper, we argue that existing SS methods cannot be reliably applied to autonomous driving system as they ignore the different importance levels of distinct classes for safedriving. For example, pedestrians in the scene are much more important than sky when driving a car, so their segmentations should be as accurate as possible. To incorporate the importance information possessed by various object classes, this paper designs an “Importance-Aware Loss” (IAL) that specifically emphasizes the critical objects for autonomous driving. IAL operates under a hierarchical structure, and the classes with different importance are located in different levels so that they are assigned distinct weights. Furthermore, we derive the forward and backward propagation rules for IAL and apply them to deep neural networks for realizing SS in intelligent driving system. The experiments on CamVid and Cityscapes datasets reveal that by employing the proposed loss function, the existing deep learning models including FCN, SegNet and ENet are able to consistently obtain the improved segmentation results on the pre-defined important classes for safe-driving.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Unsupervised Model Adaptation for Continual Semantic Segmentation (2020, methodology): “We have also included methods that are based on direct distributional matching which are more similar to MAS3: FCNs in the Wild (Hoffman et al. 2016), CDA (Zhang, David, and Gong 2017), DCAN (Wu et al. 2018), SWD (Lee et al. 2019), Cross-City (Chen et al. 2017b).”