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.
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.
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.
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.
