Research Article
Light Deep Learning based Edge Safety Surveillance
@INPROCEEDINGS{10.4108/eai.27-8-2020.2295046, author={Yimo Lou and Wengang Cao and Zhimin He and Guan Gui}, title={Light Deep Learning based Edge Safety Surveillance}, proceedings={Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace}, publisher={EAI}, proceedings_a={MOBIMEDIA}, year={2020}, month={11}, keywords={intelligence safety surveillance centernet mobilenet-v2 marginal devices}, doi={10.4108/eai.27-8-2020.2295046} }- Yimo Lou
Wengang Cao
Zhimin He
Guan Gui
Year: 2020
Light Deep Learning based Edge Safety Surveillance
MOBIMEDIA
EAI
DOI: 10.4108/eai.27-8-2020.2295046
Abstract
Safety is considered as the first important factor in many industries such as construction sites. Hence, artificial intelligence based safety surveillance techniques have been received strong attentions in recent years. Conventional surveillance systems for monitoring whether the workers wearing helmets are not easy to install and carry, and the largest trouble is that the system needs considerable computation, which is not that simple to satisfy the requirement of hardware. Considering the characteristic about construction sites, in this paper, we proposed a new system based on CenterNet with MobileNet-V2 as backbone. It has a video camera, a marginal device embedded with Jetson TX2 and wireless communication routers to ensure real-time transmission about live-scene about construction sites. After inspection, the light-weight network we proposed can be run in portable marginal device smoothly and stably with slight loss of average precision.
