Evaluation of Airport Runway Blockade Effectiveness Based on Improved Convolutional Neural Network

In response to the inefficiency and inability to utilize image data inherent in traditional airport runway blockade effectiveness assessment methods, this paper presents an improved convolutional neural network algorithm for assessing the blockade effectiveness of airport runways. A nonlinear model is established between the damage data of the airport runway and the success of blockade, avoiding the excessive time consumption caused by direct iteration. The size and number of convolutional kernels are modified according to the characteristics of the runway damage images, and batch normalization layers and Mish activation functions are introduced to address the issue of gradient disappea-rance during training. Simulation results demonstrate that the algorithm can effectively determine whether the runway is successfully blocked and calculate the blockade probability for a set of aiming points, and it has a significant advantage in recognition speed compared to traditional algorithms.