Research on the Application of Pruning Algorithm Based on Local Linear Embedding Method in Traffic Sign Recognition

Efficient traffic sign recognition is crucial to facilitating the intelligent driving of new energy vehicles. However, current approaches like the Vision Transformer (ViT) model often impose high storage and computational demands, escalating hardware costs. This paper presents a similarity filter pruning method based on locally linear embedding. Using the alternating direction multiplier method and the loss of the locally linear embedding method for the model training function, the proposed pruning method prunes the operation model mainly by evaluating the similarity of each layer in the network layer filters. According to the pre-set pruning threshold value, similar filters to be pruned are obtained, and the filter with a large cross-entropy value is retained. The results from the Belgium Traffic Sign (BelgiumTS) and German Traffic Sign Recognition Benchmark (GTSRB) datasets indicate that the proposed similarity filter pruning based on local linear embedding (SJ-LLE) pruning algorithm can reduce the number of parameters of the multi-head self-attention module and Multi-layer Perceptron (MLP) module of the ViT model by more than 60%, and the loss of model accuracy is acceptable. The scale of the ViT model is greatly reduced, which is conducive to applying this model in embedded traffic sign recognition equipment. Also, this paper proves the hypothesis through experiments that “using the LLE algorithm as the loss function for model training before pruning plays a positive role in reducing the loss of model performance in the pruning process”.