DFNet: Semantic Segmentation on Panoramic Images with Dynamic Loss Weights and Residual Fusion Block
Wei Jiang, Yan Wu, Linting Guan, Junqiao Zhao
Abstract
For the domain of self-driving and automatic parking, perception is a basic and critical technique, moreover, the detection of lane markings and parking slots is an important part of visual perception. Compared with front sight images, panoramic images(PI) can capture more comprehensive pavement information. However, the imbalance of different classes in PI is even more serious. Additionally, the judgment of boundary information between areas is a hard problem in deep models. Therefore, we propose a new model named DFNet to solve these problems. The proposed model has two main contributions, one is dynamic loss weights, and the other is residual fusion block(RFB). DFNet use dynamic loss weights to overcome the negative effect of imbalance dataset, which are calculated according to the pixel number of each class in a batch. RFB is composed of several convolutional layers, a pooling layer, and a fusion layer to combine the feature maps by pixel multiplication, which can reduce boundary information loss. We evaluate our method on PSV dataset, and the achieved advanced results demonstrate the effectiveness of the proposed model.
BibTeX
@inproceedings{icra2019_dfnetsemanticseg,
title = {DFNet: Semantic Segmentation on Panoramic Images with Dynamic Loss Weights and Residual Fusion Block},
author = {Wei Jiang and Yan Wu and Linting Guan and Junqiao Zhao},
booktitle = {ICRA 2019},
year = {2019}
}