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It is of great significance to effectively identify the flame-burning state of cement rotary kilns to optimize the calcination process and ensure the quality of cement. However, high-temperature and smoke-filled environments bring about difficulties with respect to accurate feature extraction and data acquisition. To address these challenges, this paper proposes a novel approach. First, an improved denoising diffusion probability model (RE-DDPM) is proposed. By applying a mask to the burning area and mixing it with the actual image in the denoising process, local diversity generation in the image was realized, and the problem of limited and uneven data was solved. Secondly, this article proposes the DAF-FasterNet model, which incorporates a deformable attention mechanism (DAS) and replaces the ReLU activation function with FReLU so that it can better focus on key flame features and extract finer spatial details. The RE-DDPM method exhibits faster convergence and lower FID scores, indicating that the generated images are more realistic. DAF-FasterNet achieves 98.9% training accuracy, 98.1% test accuracy, and a 22.3 ms delay, making it superior to existing methods in flame state recognition. |
