Airport Surface Aircraft Object Detection Based on Improved YOLOX
The real-time monitoring of aircraft on the airport surface is the basis of the remote tower system.In order to achieve fast and accurate detection of aircraft on the airport surface,a method for aircraft object detection on the airport surface based on YOLOX fusion attention mechanism is proposed.The Convolutional Block Attention Mod-ule was introduced into the enhanced feature extraction network to increase the attention to the spatial position and features of the aircraft target.At the same time,the Complete Intersection over Union method was used to calculate the regression loss function of the detection frame,and a comparative experiment was carried out on YOLOX and the im-proved model based on the Tensorflow.The results show that the YOLOX model has high detection accuracy and speed.The mAP0.5 of the proposed YOLOX-CT and YOLOX-CS models reach 97.34%and 97.28%,respectively,and the FPS reach 46 and 35.The improved model based on YOLOX has high efficiency for aircraft object detection,which can ensure the safety of airport operation and improve operation efficiency.
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