###### 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.  
mAPpl. Sci. 2024 14 2024  
To achieve more robust material classification, researchers have recognized the value of leveraging information from multiple wavelengths [5]. The electromagnetic spectrum encompasses various regions defined by wavelength, as depicted in Figure 3. Among these, short-wave infrared (SWIR) imaging, operating in the 1000 nm to 2500 nm range, offers distinct advantages for material analysis. SWIR's high transmissivity and capability to penetrate obscurants like fog, smoke, and dust, combined with materials' unique reflection and absorption characteristics in this range, enables differentiation even when objects appear identical in the visible spectrum.  
In this paper, we are proposing a method to address this challenge by leveraging differences in light reflection and absorption characteristics across various wavelengths. The proposed approach utilizes deep learning, training an object detection model with multi-wavelength data to enable the recognition of objects that are visually similar but materially  
Figure 1: Human and mannequin in traffic environment.  
Figure 3: Illustration of the electromagnetic spectrum with an emphasis on the infrared range.  
Figure 2: Other objects with similar appearances: the red box indicates a real person, and the yellow box indicates a human-shaped object.  
distinct. Furthermore, we investigate which wavelengths beyond the visible range can be effective in differentiating similar-looking objects based on their material properties. After confirming the feasibility of material-based differentiation through an algorithm-based method to select the correct wavelengths, we use these wavelengths to train an object detection model. The proposed approach aims to mitigate object misidentification during autonomous driving, addressing scenarios like the misinterpretation of traffic light reflections or human figures in advertisements as real objects, as shown in Figure 2. Such misinterpretations, common in autonomous driving, underscore the need for vision systems that incorporate information from diverse wavelength ranges.  
The contributions of this paper are as follows:  
1. We assessed multiple SWIR-range wavelengths to determine which one has advantages in distinguishing materials that contain moisture.
2. We propose a method to distinguish materials by utilizing the radiation characteristics in the SWIR range.
3. To detect objects required for autonomous driving, we proposed a deep learning method that uses the best-performing wavelengths with a multi-modal fusion approach to differentiate and identify objects with similar appearances.