Panoptic segmentation of UAV images with deformable convolution network and mask scoring

Hongwei Chen; Laihui Ding; Fengqin Yao; Pengfei Ren; Shengke Wang

doi:10.1117/12.2589337

27 January 2021 Panoptic segmentation of UAV images with deformable convolution network and mask scoring

Hongwei Chen, Laihui Ding, Fengqin Yao, Pengfei Ren, Shengke Wang

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 1172013 (2021) https://doi.org/10.1117/12.2589337
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Panoptic segmentation is an important method for UAV platforms to implement road condition monitoring and urban planning. In recent years, the panoptic segmentation technology provides more comprehensive information than the current semantic segmentation technology. In this paper, the framework of the panoptic segmentation algorithm is designed for the UAV application scenario. Due to the large target scene and small target of UAV, resulting in the lack of foreground targets in the segmentation results and the poor quality of the segmentation mask. To solve these problems, this paper introduces deformable convolution in the feature extraction network to improve the ability of network feature extraction. In addition, the MaskIoU module is introduced in the instance segmentation branch to improve the overall quality of the foreground target mask. In this paper, a series of data are collected by UAV and organized into UAV_OUC panoptic segmentation dataset. We tested on the UAV_OUC panoptic segmentation dataset. The experimental results on UAV_OUC panoptic benchmark validate the effectiveness of our proposed method.

Citation Download Citation

Hongwei Chen, Laihui Ding, Fengqin Yao, Pengfei Ren, and Shengke Wang "Panoptic segmentation of UAV images with deformable convolution network and mask scoring", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 1172013 (27 January 2021); https://doi.org/10.1117/12.2589337

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