Intelligent Detection of "Problematic Map" Using Convolutional Neural Network

  Objectives  In order to solve the problem of low efficiency of manual visual discrimination in the audit of "problematic map" in China, an end-to-end adaptive detection method for small sample scene based on the multi-scale feature fusion of convolutional neural network (CNN) is proposed in this paper.

  Methods  The real-time enhancement of the dataset can overcome the shortcoming of CNN, which requires a large number of training samples. By fusing multiple maps at different scales, the intelligent detection of significant error areas of the "problematic map" in multiple scales is realized. The region proposal network is optimized by taking the attributes of wrong regions into account to further improve the detection accuracy.

  Results  Compared with the existing detection method, the accuracy of the proposed method is increased by 8 times, which verifies its effectiveness.

  Conclusions  The proposed method provides a new solution for large-scale "problematic map" detection, and can be quickly applied in production.