Detection of typical geographic object in maps based on deep learning

doi:10.13474/j.cnki.11-2246.2022.0328

Abstract

Abstract: Aiming at the problem of typical geographic target recognition in maps, this paper introduces two object detection methods based on deep learning: YOLO and RetinaNet which replaces cross entropy loss function with focal loss. Images are input into two neural network models for training and testing, and object detection results are compared and analyzed. The results show that the RetinaNet model has significantly improved the accuracy of object detection on maps, and the running speed is still up to seconds. The high accuracy and efficiency of the geographic object detection method can save a lot of manpower and time costs during map review, providing a new technical reference for intelligent understanding of map content and Internet map supervision.

Key words: geographic object detection, deep learning, convolutional neural network, YOLO, RetinaNet

CLC Number:

P208

WANG Zheng, FU Xiao, DU Kaixuan, LIU Jiping, CHE Xianghong. Detection of typical geographic object in maps based on deep learning[J]. Bulletin of Surveying and Mapping, 2022, 0(11): 74-78.

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