VideoMamba Enhanced with Attention and Learnable Fourier Transform for Superheat Identification

Superheat degree (SD) is an important indicator for identifying the status of aluminum electrolytic cells. The fire hole video of the aluminum electrolytic cell captured by an industrial camera is an important basis for identifying SD. This article proposes a novel method that VideoMamba enhances with attention and learnable Fourier transform (CFVM) for SD identification. With a lower computational complexity and feature extraction capabilities comparable to transformers, VideoMamba offers the CFVM model a stronger feature extraction basis. The channel attention mechanism (CAM) block can achieve information exchange between channels. Through matrix eigenvalue manipulation, the learnable nonlinear Fourier transform (LNFT) block may guarantee stable convergence of the model. Furthermore, the LNFT block can efficiently use mixed frequency domain channels to capture global dependency information. The model is trained using the aluminum electrolysis fire hole dataset. Compared with recent fire hole identification models that primarily rely on neural networks, the method proposed in this paper is based on the concept of state space modeling, offering lower model complexity and enhanced feature extraction capability. Experimental results demonstrate that the proposed model achieves competitive performance in fire hole video identification tasks, reaching an identification accuracy of 85.7% on the test set.