[1]WANG Zihao,XIA Xiushan,CAO Yang,et al.Multimodal sequence-based petrochemical VOCs plume semantic segmentation[J].CAAI Transactions on Intelligent Systems,2025,20(6):1420-1431.[doi:10.11992/tis.202501034]
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Multimodal sequence-based petrochemical VOCs plume semantic segmentation

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 6 Page number: 1420-1431 Column: 学术论文—机器感知与模式识别 Public date: 2025-11-05
Title:
Multimodal sequence-based petrochemical VOCs plume semantic segmentation
Author(s):
WANG Zihao1;  XIA Xiushan1;  CAO Yang2;  ZHANG Kunyu3
1. Institute of Advanced Technology, University of Science & Technology of China, Hefei 230031, China;
2. Department of Automation, University of Science & Technology of China, Hefei 230027, China;
3. Institute of Artificial Intelligence, Hefei Comp
Keywords:
VOCs plume; gas detectors; semantic segmentation; motion information; diffusion; multimodal feature fusion; infrared imaging; blurred edge
CLC:
TP391
DOI:
10.11992/tis.202501034
Abstract:
Petrochemical volatile organic compounds (VOCs) plumes manifest distorted and changeable shapes, blurred edges, and translucency under infrared imaging. The implementation of existing image semantic segmentation methods in the direct application context presents significant challenges in the extraction of gas features, resulting in suboptimal outcomes. To address this, this paper proposes a multimodal petrochemical VOCs plume segmentation method (MPPS) that incorporates contextual sequences. Initially, the diffusion characteristics of the plume edge are utilized to extract the motion diffusion vectors of the previous and subsequent frames of the target frame. Subsequently, the edge features of the VOC plume are enhanced by superimposing motion information. Second, an adaptive weight module is designed to leverage the non-imaging characteristics of VOCs in visible light. This module fuses visible and infrared image features, further enhancing plume features and filtering background interference. Finally, a region-based proxy plume segmentation decoder is introduced to enhance the correlation between edge and center features of the plume while reducing the computational load of plume segmentation. Furthermore, this paper constructs a visible and infrared petrochemical VOCs video dataset. Experimental results on this dataset demonstrate that MPPS improves computational efficiency by 1.81 frames per second and segmentation accuracy by 3.53% compared to baseline networks.
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