Short-Wave Infrared (SWIR) Imaging for Robust Material Classification: Overcoming Limitations of Visible Spectrum Data

Abstract

This paper presents a novel approach to material classification using short-wave infrared (SWIR) imaging, aimed at applications where differentiating visually similar objects based on material properties is essential, such as in autonomous driving. Traditional vision systems, relying on visible spectrum imaging, struggle to distinguish between objects with similar appearances but different material compositions. Our method leverages SWIR’s distinct reflectance characteristics, particularly for materials containing moisture, and demonstrates a significant improvement in accuracy. Specifically, SWIR data achieved near-perfect classification results with an accuracy of 99% for distinguishing real from artificial objects, compared to 77% with visible spectrum data. In object detection tasks, our SWIR-based model achieved a mean average precision (mAP) of 0.98 for human detection and up to 1.00 for other objects, demonstrating its robustness in reducing false detections. This study underscores SWIR’s potential to enhance object recognition and reduce ambiguity in complex environments, offering a valuable contribution to material-based object recognition in autonomous driving, manufacturing, and beyond.