| Title: |
Deep Learning Based SWIR Object Detection in Long-Range Surveillance Systems: An Automated Cross-Spectral Approach |
| Authors: |
Miloš S. Pavlović; Petar D. Milanović; Miloš S. Stanković; Dragana B. Perić; Ilija V. Popadić; Miroslav V. Perić |
| Source: |
Sensors, Vol 22, Iss 7, p 2562 (2022) |
| Publisher Information: |
MDPI AG, 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Chemical technology |
| Subject Terms: |
SWIR imaging; object detection; deep learning; cross-spectral data annotation; multi-sensor imaging system; Chemical technology; TP1-1185 |
| Description: |
SWIR imaging bears considerable advantages over visible-light (color) and thermal images in certain challenging propagation conditions. Thus, the SWIR imaging channel is frequently used in multi-spectral imaging systems (MSIS) for long-range surveillance in combination with color and thermal imaging to improve the probability of correct operation in various day, night and climate conditions. Integration of deep-learning (DL)-based real-time object detection in MSIS enables an increase in efficient utilization for complex long-range surveillance solutions such as border or critical assets control. Unfortunately, a lack of datasets for DL-based object detection models training for the SWIR channel limits their performance. To overcome this, by using the MSIS setting we propose a new cross-spectral automatic data annotation methodology for SWIR channel training dataset creation, in which the visible-light channel provides a source for detecting object types and bounding boxes which are then transformed to the SWIR channel. A mathematical image transformation that overcomes differences between the SWIR and color channel and their image distortion effects for various magnifications are explained in detail. With the proposed cross-spectral methodology, the goal of the paper is to improve object detection in SWIR images captured in challenging outdoor scenes. Experimental tests for two object types (cars and persons) using a state-of-the-art YOLOX model demonstrate that retraining with the proposed automatic cross-spectrally created SWIR image dataset significantly improves average detection precision. We achieved excellent improvements in detection performance in various variants of the YOLOX model (nano, tiny and x). |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1424-8220 |
| Relation: |
https://www.mdpi.com/1424-8220/22/7/2562; https://doaj.org/toc/1424-8220 |
| DOI: |
10.3390/s22072562 |
| Access URL: |
https://doaj.org/article/4875c5eb5ffd48e0962e4e5bc0c9bc85 |
| Accession Number: |
edsdoj.4875c5eb5ffd48e0962e4e5bc0c9bc85 |
| Database: |
Directory of Open Access Journals |