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Deep Learning Based Feature Selection Algorithm for Small Targets Based on mRMR
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DOI：10.3390/mi13101765.png)
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摘要
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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.
Keyword:
feature extraction
minimum redundancy maximum relevance
deep learning network
image pre-processing
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3
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1.4W
被引数:
2.9W
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Semi-supervised feature selection with minimal redundancy based on local adaptive.png)
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