A real-time intelligent monitoring method for indoor evacuee distribution based on deep learning and spatial division

Han, Litao; Feng, Hengjian; Liu, Guoyu; Zhang, Aiguo; Han, Tao

doi:10.1016/j.jobe.2024.109764

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Other1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Deep learning models for vision-based occupancy detection in high occupancy buildings

Zhang,

Calautit,

Tien

et al. 2024

Journal of Building Engineering

View full text Add to dashboard Cite

Deep learning models for vision-based occupancy detection in high occupancy buildings

Zhang,

Calautit,

Tien

et al. 2024

Journal of Building Engineering

View full text Add to dashboard Cite

Attendance Tracking System using Many Battery-free Photovoltaic Bluetooth Beacon Badges

Wu,

Che,

Jin

et al. 2024

Proceedings of the 12th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems

View full text Add to dashboard Cite

Complex Indoor Human Detection with You Only Look Once: An Improved Network Designed for Human Detection in Complex Indoor Scenes

Xu,

2024

Applied Sciences

View full text Add to dashboard Cite

Indoor human detection based on artificial intelligence helps to monitor the safety status and abnormal activities of the human body at any time. However, the complex indoor environment and background pose challenges to the detection task. The YOLOv8 algorithm is a cutting-edge technology in the field of object detection, but it is still affected by indoor low-light environments and large changes in human scale. To address these issues, this article proposes a novel method based on YOLOv8 called CIHD-YOLO, which is specifically designed for indoor human detection. The method proposed in this article combines the spatial pyramid pooling of the backbone with an efficient partial self-attention, enabling the network to effectively capture long-range dependencies and establish global correlations between features, obtaining feature information at different scales. At the same time, the GSEAM module and GSCConv were introduced into the neck network to compensate for the loss caused by differences in lighting levels by combining depth-wise separable convolution and residual connections, enabling it to extract effective features from visual data with poor illumination levels. A dataset specifically designed for indoor human detection, the HCIE dataset, was constructed and used to evaluate the model proposed in this paper. The research results show that compared with the original YOLOv8s framework, the detection accuracy has been improved by 2.67%, and the required floating-point operations have been reduced. The comprehensive case analysis and comparative evaluation highlight the superiority and effectiveness of this method in complex indoor human detection tasks.

show abstract

A real-time intelligent monitoring method for indoor evacuee distribution based on deep learning and spatial division

Cited by 3 publications

References 54 publications

Deep learning models for vision-based occupancy detection in high occupancy buildings

Deep learning models for vision-based occupancy detection in high occupancy buildings

Attendance Tracking System using Many Battery-free Photovoltaic Bluetooth Beacon Badges

Complex Indoor Human Detection with You Only Look Once: An Improved Network Designed for Human Detection in Complex Indoor Scenes

Contact Info

Product

Resources

About