Study on target recognition of USV based on multi-mode composite detection method
With the continuous development and application of USV technology,its threat to ships is increasing.In or-der to improve the recognition performance of multi-component laser/infrared/millimeter wave detector on small sur-face targets,a composite detection signal recognition method MCCNN-XGB based on multi-channel convolutional neu-ral network(Multi-Channel Convolutional Neural Network,MCCNN)and extreme gradient lifting decision tree(Ex-treme Gradient Boosting,XGBoost)is proposed.At the same time,a single channel CNN recognition network and XG-Boost recognition algorithm based on artificial feature extraction are constructed as a comparison.Then,the target rec-ognition performance of the above three models is evaluated and compared through the test data of UAV mount USV target.The test results show that the recognition algorithm based on MCCNN-XGB performs the best,with a test accu-racy of 97.26％.The recognition method proposed in this paper can effectively extract the features of the complex de-tection signal,and can reduce the false recognition rate and missing recognition rate,which has a good recognition effect.
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