Aircraft type recognition algorithm based on SKNet attention mechanism
Aircraft type recognition is a type of fine-grained image classification that focuses on designing neural network models capable of discerning subtle and distinctive features among various aircraft types.In response to challenges such as a large number of aircraft categories,subtle inter-class differences,and significant intra-class variations in aircraft recognition tasks,an aircraft type recognition algorithm is proposed based on improved SKNet(Selective Kernel Network)attention and data augmentation.The ResNeXt101 network utilized as the base network,improves the CBAM(Convolutional Block Attention Module)attention,and introduces a parallel channel-spatial attention called PCSA(Parallel Channel-Spatial Attention)embedded with different branches of selectable convolution modules,resulting in a new convolution module called PCSA-SK,which is integrated into the base network to further fuse and allocate weights to the deep-level features extracted by the base network.According to the region with discriminative information in the target activation map,the discriminative region is cropped on the original image and added to the training set to achieve data augmentation.Experimental results demonstrate that the proposed algorithm achieves a recognition accuracy of 93.57%on the FGVC-Aircraft dataset,outperforming other aircraft recognition algorithms.
aircraft type recognitionSKNet attentiondata augmentation
国家科技期刊平台
NSTL
万方数据
维普
