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Gas plume detection in infrared image using mask R-CNN with attention mechanism
Author(s): Xin Peng; Hanlin Qin; Zhuangzhuang Hu; Binbin Cai; Jin Liang; Hongxuan Ou
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Paper Abstract

The detection of chemical plumes is a challenging task in the field of infrared image detection due to the diffusivity of gas plumes. As a general-purpose segmentation architecture, Mask R-CNN can output high-quality instance segmentation masks while efficiently detecting gases. However, Mask R-CNN cannot achieve accurate segmentation of deformable targets. Therefore, in this paper, an infrared image gas plume detection method based on the attention mechanism Mask R-CNN is proposed, which can effectively detect the gas plume in the image and segment the infrared image. First, the preprocessed image is imported into Feature Pyramid Networks (FPN) to obtain the corresponding feature map. Second, the feature map is sent to the regional offer network (RPN) to obtain candidate RoIs. Then, a ROI Align operation is performed on the candidate ROI. Finally, these ROIs are classified, Bounding-box regression, and Mask generation. And we attach the edge attention mechanism to the mask branch of Mask R-CNN to improve the detection accuracy. The experimental results show that the method is validated on the real infrared gas images, and competitive results with the prior art methods.

Paper Details

Date Published: 18 December 2019
PDF: 6 pages
Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 113420U (18 December 2019); doi: 10.1117/12.2548179
Show Author Affiliations
Xin Peng, Xidian Univ. (China)
Hanlin Qin, Xidian Univ. (China)
Zhuangzhuang Hu, Xidian Univ. (China)
Binbin Cai, Xidian Univ. (China)
Jin Liang, Xidian Univ. (China)
Hongxuan Ou, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 11342:
AOPC 2019: AI in Optics and Photonics
John Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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