Affiliations: [a] College of Art, Northeastern University, Shenyang, Liaoning, China
| [b] School of Equipment Engineering, Shenyang Ligong University, Shenyang, Liaoning, China
| [c] School of Architectural Planning and Design, University of Sydney, Sydney, Australia
Correspondence:
[*]
Corresponding author. Yang Xiaozhou, College of Art, Northeastern University, Shenyang 110189, Liaoning, China. E-mail: [email protected].
Abstract: Based on the impact of epidemic prevention and control, the floating population supervision department classifies and controls the floating population by industry. There are many personnel management and control points. When the computer-aided management system is used, the outdoor environment is complex and the data interference is large. Therefore, the recognition accuracy of outdoor scenery is required to be higher. In this paper, a convolutional neural network with adaptive weights is proposed. In this method, the feature fusion strategy is combined with the network, and the optimal feature weight is obtained by training the network. In addition, this paper uses multiple two classifiers instead of multiple classifiers to achieve accurate target classification. Experiments show that the method proposed in this paper has excellent performance in the detection of similar objects. The strategy of replacing multi classification network with multi classification network improves the accuracy and recall of target detection in known environment.
