Research On Detection And Tracking Algorithm Of Moving Objects In Airport Scene Based On Deep Learning

In the construction process of smart civil aviation,the construction of intelligent monitoring is of great significance,and the airport surface aircraft detection and tracking technology is the basis of airport intelligent monitoring,and has broad application prospects.Compared with traditional image processing technology,detection and tracking technology based on deep learning has better performance and has achieved good results in many fields.In the field of civil aviation,due to the complex traffic conditions on the airport surface,the changing lighting conditions,and the occlusion of buildings,there are still problems such as low detection accuracy and poor detection effect of occluded aircraft.In view of these problems,this thesis studies the detection and tracking technology of aircraft on the airport surface.In order to improve the detection accuracy of aircraft on the airport surface,this thesis proposes an improvement measure combined with the attention mechanism based on the YOLOv5(You Only Look Once v5)algorithm.By allowing the model to distinguish the importance of different channels,the detection accuracy is improved,and The detection speed meets the requirements of real-time detection.Aiming at the problem that occluded aircraft is prone to missed detection,this thesis optimizes the Non-Maximum Suppression(NMS)algorithm to reduce the probability of missed detection and false detection.And the robustness test is carried out with the improved algorithm to verify the stability of the algorithm.Then,the improved algorithm is combined with the DeepSort algorithm based on deep learning to realize the tracking of the aircraft,and the tracking effect is improved.In addition,this thesis uses the surveillance video of Chengdu Shuangliu International Airport to produce a total of 3866 images of the airport scene aircraft dataset Aeroplane,which includes various scenes of the airport scene(tarmac,runway,etc.)and aircraft with various attitudes under weather and light conditions.Picture,which enhances the performance of the algorithm in practical application.In order to optimize the model more conveniently,this thesis also develops a visual experiment platform based on PyQt5,which avoids direct contact with complex source code and improves the experimental efficiency.