@article{ref1,
title="A thermal infrared pedestrian-detection method for edge computing devices",
journal="Sensors (Basel)",
year="2022",
author="You, Shuai and Ji, Yimu and Liu, Shangdong and Mei, Chaojun and Yao, Xiaoliang and Feng, Yujian",
volume="22",
number="17",
pages="e6710-e6710",
abstract="The thermal imaging pedestrian-detection system has excellent performance in different lighting scenarios, but there are problems regarding weak texture, object occlusion, and small objects. Meanwhile, large high-performance models have higher latency on edge devices with limited computing power. To solve the above problems, in this paper, we propose a real-time thermal imaging pedestrian-detection method for edge computing devices. Firstly, we utilize multi-scale mosaic data augmentation to enhance the diversity and texture of objects, which alleviates the impact of complex environments. Then, the parameter-free attention mechanism is introduced into the network to enhance features, which barely increases the computing cost of the network. Finally, we accelerate multi-channel video detection through quantization and multi-threading techniques on edge computing devices. Additionally, we create a high-quality thermal infrared dataset to facilitate the research. The comparative experiments on the self-built dataset, YDTIP, and three public datasets, with other methods show that our method also has certain advantages.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s22176710",
url="http://dx.doi.org/10.3390/s22176710"
}