Gas plume detection in infrared image using mask R-CNN with attention mechanism

Xin Peng; Hanlin Qin; Zhuangzhuang Hu; Binbin Cai; Jin Liang; Hongxuan Ou

doi:10.1117/12.2548179

18 December 2019 Gas plume detection in infrared image using mask R-CNN with attention mechanism

Xin Peng, Hanlin Qin, Zhuangzhuang Hu, Binbin Cai, Jin Liang, Hongxuan Ou

Proceedings Volume 11342, AOPC 2019: AI in Optics and Photonics; 113420U (2019) https://doi.org/10.1117/12.2548179
Event: Applied Optics and Photonics China (AOPC2019), 2019, Beijing, China

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.

Citation Download Citation

Xin Peng, Hanlin Qin, Zhuangzhuang Hu, Binbin Cai, Jin Liang, and Hongxuan Ou "Gas plume detection in infrared image using mask R-CNN with attention mechanism", Proc. SPIE 11342, AOPC 2019: AI in Optics and Photonics, 113420U (18 December 2019); https://doi.org/10.1117/12.2548179

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available