Related papers: Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data

Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data

URL: http://arxiv.org/abs/2311.06797v1
Date: Sun, 12 Nov 2023 10:19:14 GMT
Title: Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data
Authors: Chenyang Bi, Yueyang Li and Haichi Luo
Abstract summary: Multi-modal industrial anomaly detection based on 3D point clouds and RGB images is just beginning to emerge. The above methods require a longer inference time and higher memory usage, which cannot meet the real-time requirements of the industry. We propose a lightweight dual-branch reconstruction network based on RGB-D input, learning the decision boundary between normal and abnormal examples.
Score: 1.861332908680942
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Unsupervised anomaly detection methods are at the forefront of industrial anomaly detection efforts and have made notable progress. Previous work primarily used 2D information as input, but multi-modal industrial anomaly detection based on 3D point clouds and RGB images is just beginning to emerge. The regular approach involves utilizing large pre-trained models for feature representation and storing them in memory banks. However, the above methods require a longer inference time and higher memory usage, which cannot meet the real-time requirements of the industry. To overcome these issues, we propose a lightweight dual-branch reconstruction network(DBRN) based on RGB-D input, learning the decision boundary between normal and abnormal examples. The requirement for alignment between the two modalities is eliminated by using depth maps instead of point cloud input. Furthermore, we introduce an importance scoring module in the discriminative network to assist in fusing features from these two modalities, thereby obtaining a comprehensive discriminative result. DBRN achieves 92.8% AUROC with high inference efficiency on the MVTec 3D-AD dataset without large pre-trained models and memory banks.