Research On Infrared Dynamic Target Detection Method Under The Background Of Sea And Sky

China has vast territorial waters and abundant marine resources.In order to ensure the safety of territorial waters,it is of great significance to detect and identify dynamic targets in the sea on the motion observation platform.At present,infrared search and tracking are the most commonly methods on the motion platform.System,this type of system mainly includes target detection,target tracking and other modules.Among them,the target detection module is located at the front end of the system and is the foundation of the entire system.Its working status will directly determine the stability and reliability of the entire system.Therefore,the detection of infrared dynamic targets under the background of the sea and sky is of great significance for improving the performance of the system and enhancing the safety of the sea area.In recent years,the target detection algorithm based on deep learning has been greatly improved.Therefore,this research hopes to provide efficient and feasible solutions for infrared dynamic target detection under the background of sea and sky through the application of deep learning technology.Infrared images generated under the background of sea and sky,due to the influence of sea clutter,sea and sky background,etc.,the generated infrared images often have the characteristics of blurred image edges and complex image background.Therefore,the use of traditional target detection algorithms often results in high false alarm rates and low detection accuracy.In addition,it is often difficult to detect infrared point targets using a single infrared image detection.Therefore,in order to solve the above problems,after understanding the research background of the subject in depth,this paper proposes to use the infrared image data and radiation characteristic data of the birds,helicopters,civil aviation and other targets under the background of the sea and sky as the main data source,and use deep learning technology to construct The dual-model integrated detection algorithm for infrared dynamic targets under the background of sea and sky,the main research contents of this paper are as follows:(1)The characteristics of infrared image background,noise and dynamic targets under the background of sea and sky are studied;for the characteristics of low signal-to-noise ratio and blurred edges of infrared dynamic targets under the background of sea and sky,a composite filter algorithm is used to preprocess the image;In addition,for the target to be detected in the sea and sky background airspace target,the sea-sky-line extraction technology based on local Otsu and Hough transform is used to realize the segmentation of the sea and sky background airspace.(2)Several classic deep learning target detection models were compared and tested,and the YOLOv4 model suitable for infrared image detection in the sea and sky background was selected.Then,the YOLOv4 was detected for the characteristics of the infrared dynamic target under the sea and sky background such as poor contrast and blurred image edges.Improve the model's shortcomings: firstly,from the perspective of improving the detection accuracy,use the k-means clustering algorithm to reconstruct the priori frame of the data set;then design a multi-scale fusion algorithm based on the characteristics of infrared weak targets to improve the detection accuracy of weak targets.Finally,while maintaining the target detection speed,the image detection performance of the model is improved.The results show that the detection accuracy of the improved model is increased by 2.66%.(3)Next to the problem that it is difficult to accurately detect infrared point targets with only a single infrared image detection model,this paper introduces a radiation characteristic discrimination model to discriminate the infrared image detection model.The specific method is to combine the two The discriminant results of the model are fused using Bayesian fusion rule to complete the establishment of the integration of dual models.The results show that: compared with a single infrared image detection model,the fusion integrated model has improved the detection accuracy of civil aviation by 1.75%,and the recall rate has increased by 0.67%;the detection accuracy of helicopters has increased by 0.98%,and the recall rate has increased 0.84%;the detection accuracy of birds has increased by 2.01%,and the recall rate has increased by 0.55%.In summary,this article studies the relevant technical theories for the detection task of infrared dynamic targets under the background of sea and sky.Various models were compared and tested to build a neural network structure suitable for this task.Finally,the key technologies involved in the detection task were explored,and certain research results were obtained.