## 6 Conclusions  
In conclusion, this study demonstrates the effectiveness of SWIR (short-wave infrared) imaging for material classification, particularly in distinguishing objects that appear similar in the visible spectrum but have different physical properties. By examining objects like mannequins, humans, and banners, we highlighted how SWIR imaging can enhance object recognition in applications such as autonomous driving, where accurate differentiation is critical for safety. Our results affirm that the unique reflectance characteristics of materials in the SWIR range allow for a level of differentiation that visible-light imaging alone cannot achieve. We further validated our approach through object classification experiments involving real and artificial fruits and vegetables, which confirmed the reliability of SWIR data for material-based recognition. These findings suggest that the integration of SWIR imaging into vision systems could significantly reduce false detections in complex environments. By training a deep learning model on both classification and object detection tasks using SWIR data, we demonstrated that this technology can effectively learn and apply material-specific features, advancing material recognition capabilities in dynamic settings.  
As we continue to refine and expand our datasets, future work could focus on optimizing SWIR-based models to handle diverse environments and object types. The scalability of our approach, along with its potential for cross-domain applications in areas like agriculture, manufacturing, and robotics, underscores the versatility of SWIR imaging as a robust tool for material differentiation across varied fields. Furthermore, we plan to conduct experiments to evaluate our method under challenging environmental conditions, including scenarios with fog and fire smoke.  
Conceptualization, Y.N. and S.K.; Methodology, H.S., S.Y., Y.J. and Y.N.; Software, H.S. and I.P.; Validation, H.S.; Datta curation, H.S., S.Y., Y.J., I.P. and H.J.; Writing--original draft, H.S. and S.Y.; Writing--review & editing, Y.N.; Visualization, S.Y. and Y.N.; Supervision, Y.N. and S.K.; Project administration, S.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Evaluation Institute of Industrial Technology (KEIT) grant, funded by the Korea government (MOTIE) (RS-2022-00154651, 3D semantic camera module development capable of material and property recognition).  
Not applicable.  
The data presented in this study are openly available in RGB-SWIR Fruit and Vegetable Dataset at [[https://huggingface.co/datasets/STL-Yonsei/SWIR-Fruit_and_Vegetable_Dataset](https://huggingface.co/datasets/STL-Yonsei/SWIR-Fruit_and_Vegetable_Dataset)]([https://huggingface.co/datasets/STL-Yonsei/SWIR-Fruit_and_Vegetable_Dataset](https://huggingface.co/datasets/STL-Yonsei/SWIR-Fruit_and_Vegetable_Dataset)), accessed on 25 November 2024.  
This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (RS-2023-00236245, Development of Perception/Planning AI SW for Seamless Autonomous Driving in Adverse Weather/Unstructured Environment). Additionally, this research work was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE) of Korea and National Research Foundation (NRF) of Korea.  
The authors declare no conflicts of interest.