Deep Learning Based SWIR Object Detection in Long-Range Surveillance Systems: An Automated Cross-Spectral Approach
| Autor: | Miroslav Peric, Ilija Popadic, Milos Stankovic, Petar Milanović, Dragana Peric, Miloš Pavlović |
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| Rok vydání: | 2022 |
| Předmět: |
SWIR imaging
object detection deep learning cross-spectral data annotation multi-sensor imaging system Deep Learning ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Electrical and Electronic Engineering Biochemistry Instrumentation Atomic and Molecular Physics and Optics Analytical Chemistry |
| Zdroj: | Sensors; Volume 22; Issue 7; Pages: 2562 |
| ISSN: | 1424-8220 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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