TSMS-InceptionNeXt: A Framework for Image-Based Combustion State Recognition in Counterflow Burners via Feature Extraction Optimization
Huiling Yu1, Xibei Jia2, Yongfeng Niu1, Yizhuo Zhang1,*
1 Software Engineering, Department of Computer Science, Changzhou University, Changzhou, 213146, China
2 Electrical Engineering, Department of Computer Science, Changzhou University, Changzhou, 213146, China
* Corresponding Author: Yizhuo Zhang. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.061882
Received 05 December 2024; Accepted 24 February 2025; Published online 21 March 2025
Abstract
The counterflow burner is a combustion device used for research on combustion. By utilizing deep convolutional models to identify the combustion state of a counterflow burner through visible flame images, it facilitates the optimization of the combustion process and enhances combustion efficiency. Among existing deep convolutional models, InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt. It has garnered significant attention for its computational efficiency, remarkable model accuracy, and exceptional feature extraction capabilities. However, since this model still has limitations in the combustion state recognition task, we propose a Triple-Scale Multi-Stage InceptionNeXt (TSMS-InceptionNeXt) combustion state recognition method based on feature extraction optimization. First, to address the InceptionNeXt model’s limited ability to capture dynamic features in flame images, we introduce Triplet Attention, which applies attention to the width, height, and Red Green Blue (RGB) dimensions of the flame images to enhance its ability to model dynamic features. Secondly, to address the issue of key information loss in the Inception deep convolution layers, we propose a Similarity-based Feature Concentration (SimC) mechanism to enhance the model’s capability to concentrate on critical features. Next, to address the insufficient receptive field of the model, we propose a Multi-Scale Dilated Channel Parallel Integration (MDCPI) mechanism to enhance the model’s ability to extract multi-scale contextual information. Finally, to address the issue of the model’s Multi-Layer Perceptron Head (MlpHead)neglecting channel interactions, we propose a Channel Shuffle-Guided Channel-Spatial Attention (ShuffleCS) mechanism, which integrates information from different channels to further enhance the representational power of the input features. To validate the effectiveness of the method, experiments are conducted on the counterflow burner flame visible light image dataset. The experimental results show that the TSMS-InceptionNeXt model achieved an accuracy of 85.71% on the dataset, improving by 2.38% over the baseline model and outperforming the baseline model’s performance. It achieved accuracy improvements of 10.47%, 4.76%, 11.19%, and 9.28% compared to the Reparameterized Visual Geometry Group (RepVGG), Squeeze-erunhanced Axial Transoformer (SeaFormer), Simplified Graph Transformers (SGFormer), and VanillaNet models, respectively, effectively enhancing the recognition performance for combustion states in counterflow burners.
Keywords
Counterflow burner; combustion state recognition; InceptionNeXt; dilated convolution; channel shuffling
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