Infrared Target Detection Algorithm Based on Target Feature Distribution Enhanced Convolutional Neural Network
In order to achieve the detection of infrared weak small maritime targets and reduce the impact of low sig-nal-to-noise ratio of infrared images and small differences in infrared features between targets and backgrounds on the detec-tion results, a infrared target detection algorithm based on target feature distribution enhanced convolutional neural network ( TFD_CNN) is proposed by combining the composition, texture, and target shape features of infrared images. The algo-rithm includes target feature distribution learning and deep neural networks, which has the ability to filter out noise in infra-red images and deeply mines the edge, texture and shape information of the target in the infrared image to improve the clas-sification accuracy of the convolutional neural network. Through the experimental comparison with four other algorithms, the classification accuracy of the TFD_CNN algorithm is 96％, which is higher than other algorithms. The results show that the TFD_CNN algorithm has the ability to classify drowning persons and ships in the infrared image.
infrared weak small targettarget feature distribution learningdeep learningobject classification
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