Paper Info
Title
Illumination and Temperature-Aware Multispectral Networks for Edge-Computing-Enabled Pedestrian Detection
Abstract
Accurate and efficient pedestrian detection is crucial for the intelligent transportation system regarding pedestrian safety and mobility, e.g., Advanced Driver Assistance Systems, and smart pedestrian crosswalk systems. Among all pedestrian detection methods, the vision-based detection method is demonstrated to be the most effective in previous studies. However, the existing vision-based pedestrian detection algorithms still have two limitations that restrict their implementations, those being real-time performance as well as the resistance to the impacts of environmental factors, e.g., low illumination conditions. To address these issues, this study proposes a lightweight Illumination and Temperature-aware Multispectral Network (IT-MN) for accurate and efficient pedestrian detection. The proposed IT-MN is an efficient one-stage detector. For accommodating the impacts of environmental factors and enhancing the sensing accuracy, thermal image data is fused by the proposed IT-MN with visual images to enrich useful information when visual image quality is limited. In addition, an innovative and effective late fusion strategy is also developed to optimize the image fusion performance. To make the proposed model implementable for edge computing, the model quantization is applied to reduce the model size by 75% while shortening the inference time significantly. The proposed algorithm is evaluated by comparing it with the selected state-of-the-art algorithms using a public dataset collected by in-vehicle cameras. The results show that the proposed algorithm achieves a low miss rate and inference time at 14.19% and 0.03 seconds per image pair on GPU. Besides, the quantized IT-MN achieves an inference time of 0.21 seconds per image pair on the edge device, demonstrating the potentiality of deploying the proposed model on edge devices as a highly efficient pedestrian detection algorithm.
Year
DOI
Venue
2022
10.1109/TNSE.2021.3139335
IEEE Transactions on Network Science and Engineering
Keywords
DocType
Volume
Pedestrian detection,illumination and temperature-Aware,multispectral networks,sensor fusion,network quantization
Journal
9
Issue
ISSN
Citations 
3
2327-4697
0
PageRank 
References 
Authors
0.34
18
4
Name
Order
Citations
PageRank
Yifan Zhuang100.34
Ziyuan Pu222.06
Jia Hu300.34
Yinhai Wang429239.37