Dunhuang Buddhist Art Object Recognition Based on CenterNet

Abstract

The CenterNet recognition algorithm exhibits impressive performance in deep learning-based object recognition methods. Utilizing the DLA-34 as the backbone for object detection, it achieves higher average precision than one-stage algorithms, albeit at a slower detection speed. We propose an enhanced recognition algorithm named FPN-CenterNet to address this problem, which leverages the Feature Pyramid Networks (FPN) to refine the backbone. The goal is to maintain accuracy while improving detection speed, making it more suitable for real-time 3D reconstruction tasks. Specifically, this approach adopts a feature pyramid structure to extract features at multiple scales, promptly utilizing these features across different levels for reinforcement. The original DLA-34 network, which relies on more modules and parameters for inter-scale feature interaction, reduces recognition efficiency. The proposed algorithm first employs the improved object recognition network to detect objects within an image. Subsequently, the recognition network identifies the objects and performs convolutional operations to extract their depth features. Finally, these extracted depth features are used to render the pre-modeled models of the objects, producing the desired output. 3D reconstruction is an application of recognition results in our experiment. Experimental evaluations on the Dunhuang Buddhist Art Image dataset involve comparing the proposed method and eight existing image recognition algorithms. The assessment encompasses both recognition speed and accuracy. The results demonstrate the outstanding performance of the proposed recognition method, achieving both swift recognition and facilitating 3D reconstruction of objects.

Keywords

• Object recognition
• CenterNet
• Unity3D
• Vuforia