基于YOLOv5与Deep-SORT的机场跑道侵入告警技术研究

Research on airport runway intrusion alarm technology based on YOLOv5 and Deep-SORT

针对传统的跑道侵入告警设备自动化水平低、安装维护成本较高的问题,通过机场视频系统获取机场场面图像信息,采用YOLOv5对机场场面航空器进行检测;使用轻量化网络ShuffleNetv2对Deep-SORT算法进行优化,实现对机场场面航空器的跟踪;根据单目视频采集系统建立坐标转换和测距模型,对机场场面航空器与跑道中线的距离进行准确测量,根据地面保护区设置合适的阈值实现跑道侵入告警。实验结果表明,优化后的模型平均处理时间降低了25.64%,模拟环境下航空器距跑道中心线11、18和43 cm的测距平均误差分别为0.02、0.01和0.01 cm,跑道侵入告警准确率为95.86%,该模型实时性好、准确率高,能够有效预防跑道侵入事件的发生。

The traditional runway intrusion alarm equipment has the problems of low automation level and high cost of installation and maintenance.In this paper,the airport scene image information is obtained through the airport video system,and YOLOv5 is used to detect the airport scene aircraft.A lightweight network ShuffleNetv2 is used to optimize the Deep-SORT algorithm to track the airfield aircraft.Through the monocular video acquisition system,the coordinate transformation and ranging model is established to accurately measure the distance between the airport aircraft and the runway midline.According to the ground protection zone,runway intrusion alarms can be realized by setting an appropriate threshold.The experimental results show that the average processing time of the optimized model is reduced by 25.64%,the average ranging errors of aircraft 11,18 and 43 cm from the runway center line in the simulated environment are 0.02,0.01 and 0.01 cm,respectively,and the accuracy of runway intrusion alarm is 95.86%.The model has good real-time performance and high accuracy.This method can effectively prevent the occurrence of runway intrusion events.