In order to avoid deflagration to detonation transition during the ammunition destruction by high-energy combustion method, the internal pressure of the shell can be controlled by the fused exhaust hole on the mine shell. In this paper, the equilibrium pressure equation for the steady state combustion of the internal explosive in the open-hole shell is derived by the model referring to the zero-dimensional interior ballistic calculation method under the combustion chamber model of the solid rocket engine. According to the size of fused exhaust hole measured by mine destruction experiment, the internal steady combustion equilibrium pressure for TNT charged mine is 0.69 MPa, which is far below the literatures reported deflagration to detonation transition pressure upper limit, the calculated results are in accordance with the experiment data.

In response to the challenges of posture diversity, motion blur and small target detection in unmanned aerial vehicle (UAV) target detection in long-distance and complex scenarios, this paper proposes an improved UAV target detection model:YOLO11n-SMSH. This model significantly improves the detection performance through four core mechanisms: Firstly, a CRIE module with edge perception integration is introduced in the backbone network to enhance the ability of extracting target edge features; Secondly, a SRA-DFF network with semantic association enhancement capability is used as the neck network to achieve high-quality feature interaction and fusion; Furthermore, the NTTAA detection head is finely designed, and through the weight sharing mechanism and bidirectional parallel task alignment path, the collaboration between classification and localization tasks is effectively strengthened; Finally, the GIoU loss function is introduced, and the boundary box regression is optimized using spatial coverage, improving the model's adaptability to UAV targets. Experimental results on the DUT Anti-UAV dataset show that YOLO11n-SMSH performs excellently. Compared with the baseline model, the accuracy (P), recall rate (R), mAP₅₀ and mAP_50-95 have significantly increased by 1.5%, 3.9%, 2.6% and 2.5%, respectively. The experimental results verify the effectiveness of the multi-module collaborative optimization strategy and provide a high-performance solution for UAV target detection tasks in practical applications.