Secret target automatic recognition and decryption method for real scene 3D model texture

doi:10.13474/j.cnki.11-2246.2023.0376

Abstract

Abstract: Real-scene 3D is an important part of the country's new infrastructure and has a wide range of applications in various industries. How to promote the maximum sharing of 3D data under the premise of safety has become the demand of real-scene 3D applications. Aiming at the problem of secret-related sensitive targets in real-scene 3D textures, the traditional decryption process that relies on manual retrieval of sensitive targets and images processed by editing tools is not efficient. This paper proposes an automatic recognition and decryption method for real scene 3D model texture combined with deep learning. Firstly, search the 3D model and texture image containing the secret target through the secret areas POI; then automatically identify the sensitive target in the texture image based on the YOLOv5s network model, and use GrabCut to effectively extract the target; finally, based on the multi-scale Patch match for the texture image block make repairs. It shows that the target recognition accuracy of the method is 95.3%, which is more than 40% compared with manual processing when the whole process is used. It effectively extracts sensitive targets to achieve fast decryption, and promotes the safe sharing of real-scene 3D model data.

Key words: real scene 3D, texture decryption, target recognition, texture repair

CLC Number:

P237

XU Haiyan, GUO Weiren, LI Demin, HAO Jun, XU Gang. Secret target automatic recognition and decryption method for real scene 3D model texture[J]. Bulletin of Surveying and Mapping, 2023, 0(12): 153-158.

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