Dual-Branch Cross-Fusion Normalizing Flow for RGB-D Track Anomaly Detection
With the ease of acquiring RGB-D images from line-scan 3D cameras and the development of computer vision, anomaly detection is now widely applied to railway inspection. As 2D anomaly detection is susceptible to capturing condition, a combination of depth maps is now being explored in industrial insp...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/8/2631 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850180200263843840 |
|---|---|
| author | Xiaorong Gao Pengxu Wen Jinlong Li Lin Luo |
| author_facet | Xiaorong Gao Pengxu Wen Jinlong Li Lin Luo |
| author_sort | Xiaorong Gao |
| collection | DOAJ |
| description | With the ease of acquiring RGB-D images from line-scan 3D cameras and the development of computer vision, anomaly detection is now widely applied to railway inspection. As 2D anomaly detection is susceptible to capturing condition, a combination of depth maps is now being explored in industrial inspection to reduce these interferences. In this case, this paper proposes a novel approach for RGB-D anomaly detection called Dual-Branch Cross-Fusion Normalizing Flow (DCNF). In this work, we aim to exploit the fusion strategy for dual-branch normalizing flow with multi-modal inputs to be applied in the field of track detection. On the one hand, we introduce the mutual perception module to acquire cross-complementary prior knowledge in the early stage. On the other hand, we exploit the effectiveness of the fusion flow to fuse the dual-branch of RGB-D inputs. We experiment on the real-world Track Anomaly (TA) dataset. The performance evaluation of DCNF on TA dataset achieves an impressive AUROC score of 98.49%, which is 3.74% higher than the second-best method. |
| format | Article |
| id | doaj-art-b17eda17c96e45ddb5fee6e5ce7e16d4 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-b17eda17c96e45ddb5fee6e5ce7e16d42025-08-20T02:18:15ZengMDPI AGSensors1424-82202025-04-01258263110.3390/s25082631Dual-Branch Cross-Fusion Normalizing Flow for RGB-D Track Anomaly DetectionXiaorong Gao0Pengxu Wen1Jinlong Li2Lin Luo3School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, ChinaSchool of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, ChinaWith the ease of acquiring RGB-D images from line-scan 3D cameras and the development of computer vision, anomaly detection is now widely applied to railway inspection. As 2D anomaly detection is susceptible to capturing condition, a combination of depth maps is now being explored in industrial inspection to reduce these interferences. In this case, this paper proposes a novel approach for RGB-D anomaly detection called Dual-Branch Cross-Fusion Normalizing Flow (DCNF). In this work, we aim to exploit the fusion strategy for dual-branch normalizing flow with multi-modal inputs to be applied in the field of track detection. On the one hand, we introduce the mutual perception module to acquire cross-complementary prior knowledge in the early stage. On the other hand, we exploit the effectiveness of the fusion flow to fuse the dual-branch of RGB-D inputs. We experiment on the real-world Track Anomaly (TA) dataset. The performance evaluation of DCNF on TA dataset achieves an impressive AUROC score of 98.49%, which is 3.74% higher than the second-best method.https://www.mdpi.com/1424-8220/25/8/2631anomaly detectionnormalizing flowRGB-D fusion |
| spellingShingle | Xiaorong Gao Pengxu Wen Jinlong Li Lin Luo Dual-Branch Cross-Fusion Normalizing Flow for RGB-D Track Anomaly Detection Sensors anomaly detection normalizing flow RGB-D fusion |
| title | Dual-Branch Cross-Fusion Normalizing Flow for RGB-D Track Anomaly Detection |
| title_full | Dual-Branch Cross-Fusion Normalizing Flow for RGB-D Track Anomaly Detection |
| title_fullStr | Dual-Branch Cross-Fusion Normalizing Flow for RGB-D Track Anomaly Detection |
| title_full_unstemmed | Dual-Branch Cross-Fusion Normalizing Flow for RGB-D Track Anomaly Detection |
| title_short | Dual-Branch Cross-Fusion Normalizing Flow for RGB-D Track Anomaly Detection |
| title_sort | dual branch cross fusion normalizing flow for rgb d track anomaly detection |
| topic | anomaly detection normalizing flow RGB-D fusion |
| url | https://www.mdpi.com/1424-8220/25/8/2631 |
| work_keys_str_mv | AT xiaoronggao dualbranchcrossfusionnormalizingflowforrgbdtrackanomalydetection AT pengxuwen dualbranchcrossfusionnormalizingflowforrgbdtrackanomalydetection AT jinlongli dualbranchcrossfusionnormalizingflowforrgbdtrackanomalydetection AT linluo dualbranchcrossfusionnormalizingflowforrgbdtrackanomalydetection |