Comparative Study of Unmanned Aerial Vehicle Autonomous Landing Scene Recognition Methods Combined With Transfer Learning
If UAV has the perception of landing scene,it can improve the safety of autonomous landing in unknown environ-ment. Based on three pre-trained network models of ResNet18/50/101,this paper compares and analyzes the char-acteristics of UAV autonomous landing scene recognition models constructed by two transfer methods of freezing model parameters and fine-tuning model parameters. The experi-mental results on self-built datasets and two kinds of public da-tasets show that transfer learning can quickly construct net-work models,complete target scene recognition tasks well,and solve the problems of insufficient training data and labeled data;The transfer learning mode of fine-tuning parameters has strong robustness and good generalization performance,but the time cost is high;In order to better solve the target task,when the computer hardware resources meet the require-ments,transfer learning can be carried out by fine-tuning mod-el parameters by selecting a pre-trained model with sufficient depth and strong correlation with the target task. The results of this experiment provide a reference for the solution of scene recognition task based on transfer learning in terms of meth-ods,models,parameter settings,etc.
transfer learningunmanned aerial vehicleautonomous landingscene recognitionfine-tuning model parameters
NETL
NSTL
万方数据
