Target classification with adaptive weights based on multi-feature fusion

To solve precision forming problem of the antenna panels with large non-developable double, a target classification method with adaptive weights was proposed on the basis of fusion of convolution feature and histogram of oriented gradient (HOG) feature, which was utilized to classify the targets quickly and precisely.First of all, convolution feature was extracted through the ResNet framework, in which the OpenCV interface was increased to acquire the HOG feature of the images.The dimensions of HOG feature were enlarged to maintain the same dimensions as the convolution feature.Second, SENet module was imbedded into the ResNet framework so that the weight vectors of the convolution feature and HOG feature were calculated.The features of the images were adaptively and synchronously fused based on the convolution feature, HOG feature, and the weight vectors.An innovation binary network was established based on the multi-feature fusion.Third, the binary network was imbedded into the Faster Rcnn network to establish Faster Rcnn-HOG, in which the pre-processing detection frames of the image was acquired through the strategies of coarse detection of variable threshold and focus area of prior knowledge.Then the pre-processing detection frames was precisely judged by the proposed binary network to realize the target classification.The comparative experiments among faster Rcnn-HOG, the traditional Faster Rcnn, and another feature fusion network Net-BB-HOG were conducted.The results verify that the effect of the three methods is similar in the target classification of large categories.However, faster Rcnn-HOG is more effective in identifying small categories of the targets.The validity and correctness of the proposed method is proved. © 2020, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.