Abstract:  In traditional target recognition algorithms, the classical region proposal net (RPN) has large amount of computation and high complexity of time at extracting the target candidate region. Cascade region proposal network (CRPN) is proposed as a new search method for improving the performance of RPN, in which residual learning based deep residual network (ResNet) is also used effectively to suppress the degradation phenomenon in deep-level convolution neural networks. Aimed at the network models with different depths and parameters, a novel multi-strapdown residual network (Mu-ResNet) model, which is of less memory and lower time complexity, is designed by combining two-layer and three-layer residual learning modules. The combination model of Mu-ResNet and CRPN is used for multi-target recognition test by using the unmanned aerial vehicle (UAV) target data and PASCAL VOC data. The results have shown that nearly 2% of recognition accuracy is increased compared with the combination model of ResNet and RPN.

Key words: unmanned aerial vehicle (UAV), residual network, cascade region proposal network, target recognition