A vehicle interior emotion image classification model is proposed to enhance facial expression recognition accuracy in vehicle interiors. The model is based on attention mechanism and improved ResNet. It incorporates transfer learning with a pretrained ResNet50 model as the base architecture. A Coordinate Attention (CA) mechanism module is inserted after the max pooling layer to combine feature maps and focus on channel attention and spatial relationships. CA attention modules are added after each layer to enhance feature map expressiveness. To address class imbalance in the dataset, data augmentation and weighted loss functions are used for model optimization. Experimental results show improved recognition performance on the KMU-FED dataset with a simple and implementable structure.
Particle swarm optimization algorithm is a stochastic global optimization algorithm, inertia weight is one of its important parameters, in order to improve its easy to fall into the defects of local optimization and precocious maturity, this paper proposes a new dynamic weight particle swarm optimization algorithm. The new algorithm designs a dynamic weight based on the global optimal solution and its own optimal solution, which makes the velocity change of particles more reasonable, applies it to the classical test function, accelerates the convergence speed, and effectively improves the difficulty of falling into local optimum, and improves the global search ability.
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