MDRNet: a lightweight network for real-time semantic segmentation in street scenes
ISSN: 0144-5154
Article publication date: 25 October 2021
Issue publication date: 24 November 2021
Abstract
Purpose
This paper aims to focus on the environmental perception of unmanned platform under complex street scenes. Unmanned platform has a strict requirement both on accuracy and inference speed. So how to make a trade-off between accuracy and inference speed during the extraction of environmental information becomes a challenge.
Design/methodology/approach
In this paper, a novel multi-scale depth-wise residual (MDR) module is proposed. This module makes full use of depth-wise separable convolution, dilated convolution and 1-dimensional (1-D) convolution, which is able to extract local information and contextual information jointly while keeping this module small-scale and shallow. Then, based on MDR module, a novel network named multi-scale depth-wise residual network (MDRNet) is designed for fast semantic segmentation. This network could extract multi-scale information and maintain feature maps with high spatial resolution to mitigate the existence of objects at multiple scales.
Findings
Experiments on Camvid data set and Cityscapes data set reveal that the proposed MDRNet produces competitive results both in terms of computational time and accuracy during inference. Specially, the authors got 67.47 and 68.7% Mean Intersection over Union (MIoU) on Camvid data set and Cityscapes data set, respectively, with only 0.84 million parameters and quicker speed on a single GTX 1070Ti card.
Originality/value
This research can provide the theoretical and engineering basis for environmental perception on the unmanned platform. In addition, it provides environmental information to support the subsequent works.
Keywords
Acknowledgements
Research funding: This work was supported by the National Key Research and Development Program of China under Grant 2019YFC1511401, and the National Natural Science Foundation of China under Grant 62173038 and 61103157.
Citation
Dai, Y., Wang, J., Li, J. and Li, J. (2021), "MDRNet: a lightweight network for real-time semantic segmentation in street scenes", Assembly Automation, Vol. 41 No. 6, pp. 725-733. https://doi.org/10.1108/AA-06-2021-0078
Publisher
:Emerald Publishing Limited
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