DNS: A multi-scale deconvolution semantic segmentation network for joint detection and segmentation

• Main Authors: Feng Ning, Dong Le, Zhang Qianni, Zhang Ning, Wu Xi, Chen Jianwen
• Format: Article
• Language: English
• Published: EDP Sciences 2019-01-01
• Series: MATEC Web of Conferences
• Online Access: https://www.matec-conferences.org/articles/matecconf/pdf/2019/26/matecconf_jcmme2018_02005.pdf

Real-time semantic segmentation has become crucial in many applications such as medical image analysis and autonomous driving. In this paper, we introduce a single semantic segmentation network, called DNS, for joint object detection and segmentation task. We take advantage of multi-scale deconvolution mechanism to perform real time computations. To this goal, down-scale and up-scale streams are utilized to combine the multi-scale features for the final detection and segmentation task. By using the proposed DNS, not only the tradeoff between accuracy and cost but also the balance of detection and segmentation performance are settled. Experimental results for PASCAL VOC datasets show competitive performance for joint object detection and segmentation task.