一种用于外场试验图像的引信炸点检测方法

In the anti-jamming performance test of radio proximity fuzes, the observation and recognition of the burst point state is essential for working state evaluation and performance improvement of the fuzes. Therefore, an image target detection algorithm based on the deep neural network is proposed for fuze burst point recognition. The algorithm achieves the following novel designs in the structure and training strategy of the model: The model realizes the target feature extraction based on the high-performance backbone ConvNeXt, and uses the cross stage partial structure based on dense connection and the multi-branch structure with channel attention to improve the feature extraction capability; it also applies a task-decoupled multi-detector structure to improve detection accuracy; focus loss functions are used as the loss functions of classification and confidence, and the complete intersection over union loss function is used as the loss function of prediction box regression in the model training. The proposed algorithm achieves an average precision of 92. 7% and F1-score of 87. 4% on the real fuze burst point image dataset. The results show the superiority of the proposed algorithm over the existing typical models in the fuze burst point detection task.