Application of improved convolutional neural network in UAV safety patrol in subway protection area
Based on the remote sensing data of low-altitude unmanned aerial vehicles(UAVs),this paper applied the convolutional neural network(CNN)algorithm combining artificial intelligence and machine learning technology to automatically extract the construction project location in the subway protection area through corresponding optimization and provided a new technical method for the identification and accurate positioning of safety risk sources in the subway protection area.Firstly,this paper evaluated the experimental data and solved the problems of distinguishing small target features and reducing serious loss of spatial hierarchical information and small object information in the identification process through data sample set enhancement processing.Secondly,the unimproved CNN,namely you only look once version(YOLOV5)was used for experiments.The results show that the training effect of YOLOV5 in the experimental dataset is not good,and the detection accuracy is low.In addition,there is a phenomenon of wrong detection and missed detection.Finally,in view of the problems highlighted in the experiment,YOLOV5 is improved,and optimization measures such as network-based transfer learning and attention mechanism module are introduced to improve the detection efficiency of risk source targets and solve the problem of information overload.The average detection accuracy of the optimized YOLOV5 reaches more than 92.3%,and the comprehensive patrol involving automatic accurate identification,rapid positioning,location decoding,and information aggregation of safety risk sources in the subway protection area is realized.
万方数据
维普
