基于孪生网络和区域注意力机制的球形爆炸破片毁伤效应识别研究

In the information warfare, the damage assessment of explosive fragments is of great significance to achieve accurate strike. However, the manual acquisition of distribution and geometric information of damage areas are inefficient in the damage experiment. To this end, a lightweight image segmentation model based on siamese networks and regional attention mechanisms is proposed, which achieves the efficient and accurate recognition of small-targeted spherical explosive fragmentation damage area under small samples. The model’s ability to perceive the explosion holes is improved by introducing the siamese structure, regional attention module and multi-scale convolution module. A loss function with multiple constraints is added and the best optimizer is screened so that the model optimization is more focused on the effective information for accelerating the model convergence. A quantitative detection method for the damaged area based on the connected-domain fusion watershed algorithm is proposed to achieve the accurate identification of the overlapping case of explosion broken holes. Experimental results show that the proposed method achieves higher efficiency and accuracy compared with the current mainstream models, and the average errors in predicting the area and diameter of damage region are 4. 78% and 3. 79%, respectively. The research work provides a reference for realizing the intelligent damage assessment of explosives containing fragments.