Military equipment recognition technology based on small sample learning

DOI:	10.11809/bqzbgcxb2023.07.005
Keywords:	deep learning; small sample learning; target recognition; military equipment; weapon characteristics
Abstract:	In view of the scarcity and high cost of military equipment samples and the slow progress of target recognition research, starting from human cognitive science, this paper proposes to use civil equipment data to complete deep learning training and realize the knowledge accumulation of large samples. The pattern recognition algorithm is used to extract the characteristics of equipment and weapons, and the sample target recognition is realized. By analyzing the spatial distance of the attributes of the two samples, the convolutional neural network is used as the front end, and the SIFT algorithm is used as the back end to build a progressive learning model to realize efficient identification of various military equipment. The average confidence of tanks, aircraft fighters and warships identified by the experimental test model is 87%, 92% and 91% respectively. The target identification accuracy is determined by the number of weapon samples of military equipment. The generalization of samples can also improve the target identification rate. The proposed progressive learning model makes full use of the advantages of deep learning and pattern recognition algorithms to realize small sample target recognition in the military field.
Published:	2023-07-28
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