Multispectral pattern recognition

Operating system

Image (mathematics)

Electrical engineering

Engineering

Philosophy

Fuse (electrical)

Pattern recognition (psychology)

Artificial intelligence

Fusion

Multispectral image

Object detection

Feature (linguistics)

Consistency (knowledge bases)

Object (grammar)

Computer vision

Sensor fusion

Computer science

Linguistics

Feature extraction

Image fusion

Process (computing)

Multispectral images (e.g. visible and infrared) may be particularly useful when detecting objects with the same model in different environments (e.g. day/night outdoor scenes). To effectively use the different spectra, the main technical problem resides in the information fusion process. In this paper, we propose a new halfway feature fusion method for neural networks that leverages the complementary/consistency balance existing in multispectral features by adding to the network architecture, a particular module that cyclically fuses and refines each spectral feature. We evaluate the effectiveness of our fusion method on two challenging multispectral datasets for object detection. Our results show that implementing our Cyclic Fuse-and-Refine module in any network improves the performance on both datasets compared to other state-of-the-art multispectral object detection methods.

Multispectral Fusion for Object Detection with Cyclic Fuse-and-Refine Blocks