We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Fast multispectral deep fusion networks.
- Authors
OSIN, V.; CICHOCKI, A.; BURNAEV, E.
- Abstract
Most current state-of-the-art computer vision algorithms use images captured by cameras, which operate in the visible spectral range as input data. Thus, image recognition systems that build on top of those algorithms can not provide acceptable recognition quality in poor lighting conditions, e.g. during nighttime. Another significant limitation of such systems is high demand for computational resources, which makes them impossible to use on low-powered embedded systems without GPU support. This work attempts to create an algorithm for pattern recognition that will consolidate data from visible and infrared spectral ranges and allow near real-time performance on embedded systems with infrared and visible sensors. First, we analyze existing methods of combining data from different spectral ranges for object detection task. Based on the analysis, an architecture of a deep convolutional neural network is proposed for the fusion of multi-spectral data. This architecture is based on the single shot multi-box detection algorithm. Comparison analysis of the proposed architecture with previously proposed solutions for the multi-spectral object detection task shows comparable or better detection accuracy with previous algorithms and significant improvement of the running time on embedded systems. This study was conducted in collaboration with Philips Lighting Research Lab and solutions based on the proposed architecture will be used in image recognition systems for the next generation of intelligent lighting systems. Thus, the main scientific outcomes of this work include an algorithm for multi-spectral pattern recognition based on convolutional neural networks, as well as a modification of detection algorithms for working on embedded systems.
- Subjects
MACHINE learning; REMOTE sensing; OBJECT recognition (Computer vision); REMOTE-sensing images; COMPUTER vision
- Publication
Bulletin of the Polish Academy of Sciences: Technical Sciences, 2018, Vol 66, Issue 6, p875
- ISSN
0239-7528
- Publication type
Article
- DOI
10.24425/bpas.2018.125935