ArticleSensors (Basel, Switzerland)2026
A Method for Industrial Smoke Video Semantic Segmentation Using DeffNet with Inter-Frame Adaptive Variable Step Size Based on Fuzzy Control.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Segmenting non-rigid objects such as smoke in video requires effective utilization of temporal information, which remains challenging due to their irregular deformation and complex appearance variations. Based on our previously proposed DeffNet for industrial fumes video segmentation, this letter presents a novel adaptive frame selection algorithm that employs fuzzy logic control to dynamically optimize the temporal processing step size for the specific task of industrial smoke video segmentation. Our method quantifies inter-frame variation using the Structural Similarity Index (SSIM) and Normalized Cross-Correlation (NCC) as inputs to a fuzzy inference system. Gaussian membership functions, shaped via K-means clustering, and a five-rule fuzzy system are designed to determine the optimal step size, maximizing informative dynamic feature extraction while minimizing redundant computation. As a lightweight front-end module, the algorithm integrates seamlessly into the existing DeffNet segmentation framework without reconstructing new network architecture. Extensive experiments on a dedicated industrial smoke video dataset demonstrate that our approach effectively improves the segmentation performance of DeffNet, achieving 84.27% Intersection over Union (IoU) while maintaining a high inference speed of 39.71 FPS. This work provides an efficient and scene-specific solution for temporal modeling in industrial smoke non-rigid object segmentation and offers a practical improved strategy for DeffNet in real-time industrial smoke monitoring.
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