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Tire Pattern Image Retrieval Algorithm Based on Optimized Efficientnet

Tire pattern image has always been an important clue in the handling of traffic accident cases and criminal investigation cases. By analyzing the tire pattern information left at the scene of the accident, the investigators can quickly narrow the scope of case detection, and even lock the vehicle and personnel information. Compared with the existing deep learning networks, EfficientNet uses the method of composite scaling to effectively scale the model to obtain the retrieval accuracy under the optimal structure. In this paper, based on the EfficientNet model, we propose a tire pattern image retrieval algorithm based on optimized EfficientNet. The basic network model EfficientNet-B0 is selected to extract the tire pattern image features, and the cross-entropy loss function is used in the model training, In the model optimization, we use AdamW + Cosine annealing, a variant of Adam Gradient Descent method, to further improve the network performance and the retrieval accuracy.