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.

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Vision sensor is one of the sensors that can obtain the most abundant environmental information by a single sensor at present, but a single sensor is still limited by the influence of complex environment, which makes the obtained environmental information and actual environmental information quite different. Therefore, the fusion of infrared vision sensor and visible light vision sensor is used to express the environment in multiple dimensions after fusion, which can effectively enrich the image information and supplement the required environmental information. In this paper, two-dimensional discrete wavelet is used to process the visible image and infrared image separately. The obtained fusion image retains the information of the original image and solves the problem of artifacts and ghosting of the fusion image.