Multi-objective Identification of UAV Based on Deep Residual Network
[摘要] Beacuse of the classical RPN (Region Proposal Net) exists the defect of large computation and high time complexity when extracting targets candidate region, a search mode called CRPN (Cascade Region Proposal Network) mode was proposed to ameliorate it in this paper. In order to suppress the degradation phenomenon in deep convolutional neural network training, the residual learning theory was introduced, a novel Mu-ResNet (multi-strapdown deep residual network) was proposed. Combined the Mu-ResNet with CPRN, a network model for multi-target identification of UAV was designed and tested. Compared with the network model that combines ResNet with RPN, the identification accuracy was increased nearly 2 percentage points.
[发布日期] [发布机构] School of Information Engineering, Nanchang Hangkong University, Nanchang; 330063, China^1
[效力级别] 计算机科学 [学科分类]
[关键词] Cascade regions;Deep convolutional neural networks;Identification accuracy;Learning Theory;Network modeling;Percentage points;Region proposals;Time complexity [时效性]
