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Based on the typical mesoscopic structural characteristics of metal rubber, the mesoscopic physical mechanism was revealed through analyzing the spatial configuration and the contact mode of wire helixes for its compression deformation process. The mesoscopic structure unit based on the curved beam of variable length and the model of the contact interaction between curved beams were proposed. Combined with the distribution law of frictional contact points, a new constitutive model of metal rubber for hysteresis characteristics was established, which included its basic structure parameters such as the diameter and the elastic modulus of wire, the diameter of wire helix, and the relative density of metal rubber. The model could describe the restoring force curves of metal rubber in initial loading, unloading and repeated loading phases, and theoretically explained its elastic characteristics and dry friction damping characteristics of multipoint contact. To verify the theory model, a comparison was made between the theoretical results and the experimental results for metal rubber specimens with different relative densities. The results show that theoretic calculations are consistent with the experimental data, which will provides a theoretical basis for predicting the stiffness and damping of metal rubber and guiding its design.

Small target features are difficult to distinguish and identify in an environment with complex backgrounds. The identification and extraction of multi-dimensional features have been realized due to the rapid development of deep learning, but there are still redundant relationships between features, reducing feature recognition accuracy. The YOLOv5 neural network is used in this paper to achieve preliminary feature extraction, and the minimum redundancy maximum relevance algorithm is used for the 512 candidate features extracted in the fully connected layer to perform de-redundancy processing on the features with high correlation, reducing the dimension of the feature set and making small target feature recognition a reality. Simultaneously, by pre-processing the image, the feature recognition of the pre-processed image can be improved. Simultaneously, by pre-processing the image, the feature recognition of the pre-processed image can significantly improve the recognition accuracy. The experimental results demonstrate that using the minimum redundancy maximum relevance algorithm can effectively reduce the feature dimension and identify small target features.