On Recognition Model Of Airport Bird Target Based Deep Learning

With the progress of modern society,the field of air transportation has also ushered in rapid development,and more and more attention is paid to the use of computer technology to strengthen airport security measures.At present,bird strike is one of the important factors threatening aviation safety.If the accuracy of bird target recognition can be further improved,corresponding bird repellent measures can be taken in time,thereby effectively reducing the harm caused by bird strikes.However,in the field of target recognition,there are fewer algorithms for airport bird repelling,and the recognition effect is not good.In this paper,the target recognition model of airport birds based on deep learning is studied.The two-stage and single-stage algorithms in the convolutional neural network model are compared and analyzed,and the Faster R-CNN target recognition algorithm,which is more applicable to airport bird target recognition,is selected for research and improvement.The use of a pyramid network based on dense feature fusion solves the problem of reduced features caused by the deepening of convolutional neural networks,and improves the recognition accuracy of small target birds.The K-Means clustering algorithm is used to improve the anchor frame generation method,which enhances the applicability of the anchor frame to the bird target.The bilinear interpolation algorithm is used to improve the pooling of the region of interest,and the recognition accuracy of the bird target in the bird flock is improved.An improved non-maximum suppression algorithm is used to effectively solve the overlap problem among birds.In order to verify that the four improved methods proposed in this paper will not conflict with each other,and at the same time visually show how much contribution these methods have,they are integrated into the Faster R-CNN basic algorithm for comprehensive ablation experiments.The experimental results show that the improved model has significantly improved the recognition accuracy of small targets flying birds and bird groups,and effectively solves the problem of missing recognition caused by overlapping bird targets.