Peyvandi Amirhossein, Majidi Babak, Peyvandi Soodeh, Patra Jagdish C, Moshiri Behzad
Department of Computer Engineering, Faculty of Engineering, Khatam University, Tehran, Iran.
Emergency and Rapid Response Simulation (ADERSIM) Artificial Intelligence Group, Faculty of Liberal Arts & Professional Studies, York University, Toronto, Canada.
Multimed Tools Appl. 2022;81(16):22185-22214. doi: 10.1007/s11042-021-11654-w. Epub 2022 Jan 3.
Smart city management is facing a new challenge from littered face masks during COVID-19 pandemic. Addressing the issues of detection and collection of this hazardous waste that is littered in public spaces and outside the controlled environments, usually associated with biomedical waste, is urgent for the safety of the communities around the world. Manual management of this waste is beyond the capabilities of governments worldwide as the geospatial scale of littering is very high and also because this contaminated litter is a health and safety issue for the waste collectors. In this paper, an autonomous biomedical waste management framework that uses edge surveillance and location intelligence for detection of the littered face masks and predictive modelling for emergency response to this problem is proposed. In this research a novel dataset of littered face masks in various conditions and environments is collected. Then, a new deep neural network architecture for rapid detection of discarded face masks on the video surveillance edge nodes is proposed. Furthermore, a location intelligence model for prediction of the areas with higher probability of hazardous litter in the smart city is presented. Experimental results show that the accuracy of the proposed model for detection of littered face masks in various environments is 96%, while the speed of processing is ten times faster than comparable models. The proposed framework can help authorities to plan for timely emergency response to scattering of hazardous material in residential environments.
智慧城市管理在新冠疫情期间正面临着来自废弃口罩的新挑战。解决这种通常与生物医疗废物相关、散落在公共场所和受控环境之外的危险废物的检测和收集问题，对于全球社区的安全而言迫在眉睫。由于垃圾散落的地理空间范围非常大，而且这种受污染的垃圾对垃圾收集者来说是一个健康和安全问题，因此手动管理这种废物超出了世界各国政府的能力范围。本文提出了一个自主生物医疗废物管理框架，该框架利用边缘监测和位置智能来检测废弃口罩，并通过预测建模来应对这一问题。在这项研究中，收集了一个包含各种条件和环境下的废弃口罩的新颖数据集。然后，提出了一种用于在视频监控边缘节点上快速检测丢弃口罩的新型深度神经网络架构。此外，还提出了一种位置智能模型，用于预测智慧城市中危险垃圾出现概率较高的区域。实验结果表明，所提出的在各种环境下检测废弃口罩的模型准确率为96%，而处理速度比同类模型快十倍。所提出的框架可以帮助当局规划对居民区危险物质散落情况的及时应急响应。
