基于知识蒸馏的道路交通标志识别神经网络OA北大核心CSTPCD

Recognition of traffic signs in natural scenes is susceptible to interference from factors such as lighting,occlu-sions,and blurriness,which can affect detection accuracy.Additionally,existing deep learning models have a large num-ber of parameters and high computational complexity,resulting in longer model inference times.The article proposes a neural network architecture adaptive feature extraction-vision Transformer(AFE-ViT)based on knowledge distillation for road traffic sign recognition.The architecture consists of an adaptive feature extraction module and a lightweight vision Transformer(ViT)classifier.It combines local and global feature information in the image,and has better adaptability to road traffic sign recognition in natural scenes.Among them,the adaptive feature extraction module combines Inception-NetV1,SKNet ideas and residual structure to realize the adaptive selection of multiple receptive fields,and as the front module of ViT,it effectively improves the efficiency of feature extraction.It chooses ResNet18 as the teacher network and AFE-ViT as the student network,and uses feature-level and output-level knowledge distillation methods to distill AFE-ViT and compress model parameters.The experimental results show that the recognition accuracy of this method can reach 98.98%,and the number of model parameters is only 9.9×105,which is better than similar deep learning models.