2024
DOI: 10.1111/mice.13157
|View full text |Cite
|
Sign up to set email alerts
|

Improving single‐stage activity recognition of excavators using knowledge distillation of temporal gradient data

Ali Ghelmani,
Amin Hammad

Abstract: Single‐stage activity recognition methods have been gaining popularity within the construction domain. However, their low per‐frame accuracy necessitates additional post‐processing to link the per‐frame detections. Therefore, limiting their real‐time monitoring capabilities is an indispensable component of the emerging construction of digital twins. This study proposes knowledge DIstillation of temporal Gradient data for construction Entity activity Recognition (DIGER), built upon the you only watch once (YOWO… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 84 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?