Black Ice Detection Platform and Its Evaluation using Jetson Nano Devices based on Convolutional Neural Network (CNN)

• 영문명: Black Ice Detection Platform and Its Evaluation using Jetson Nano Devices based on Convolutional Neural Network (CNN)
• 발행기관: 한국인공지능학회
• 저자명: 강선경(Sun-Kyoung KANG) 이연우(Yeonwoo LEE)
• 간행물 정보: 『인공지능연구』Vol.11 No. 4, 1~8쪽, 전체 8쪽
• 주제분류: 복합학 > 과학기술학
• 파일형태: PDF
• 발행일자: 2023.12.31

영문 초록

In this paper, we propose a black ice detection platform framework using Convolutional Neural Networks (CNNs). To overcome black ice problem, we introduce a real-time based early warning platform using CNN-based architecture, and furthermore, in order to enhance the accuracy of black ice detection, we apply a multi-scale dilation convolution feature fusion (MsDC-FF) technique. Then, we establish a specialized experimental platform by using a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Experimental results of a real-time black ice detection platform show the better performance of our proposed network model compared to conventional image segmentation models. Our proposed platform have achieved real-time segmentation of road black ice areas by deploying a road black ice area segmentation network on the edge device Jetson Nano devices. This approach in parallel using multi-scale dilated convolutions with different dilation rates had faster segmentation speeds due to its smaller model parameters. The proposed MsCD-FF Net(2) model had the fastest segmentation speed at 5.53 frame per second (FPS). Thereby encouraging safe driving for motorists and providing decision support for road surface management in the road traffic monitoring department.

목차

1. Introduction

2. Related Work

3. Methodology

4. Simulation and Results

5. Conclusion

References

키워드

Black ice detection Convolutional Neural Network (CNN) Multi-scale dilation convolution Feature fusion Road safety