HRM-CenterNet: A High-Resolution Real-time Fittings Detection Method.*
Document Type
Conference Proceeding
Publication Date
1-1-2021
Abstract
Most successful fittings detectors are anchor-based, which is challenging to meet the lightweight and real-time requirements of the edge computing system. We propose a high-resolution real-time network HRM-CenterNet. Firstly, the lightweight MobileNetV3 is used to extract multi-level features from images. Then, to improve the resolution of the feature maps and reduce the spatial semantic information loss during the image downsampling process, a high-resolution feature fusion network based on iterative aggregation is introduced. Finally, we conduct experiments on the PASCAL VOC dataset and fittings dataset. The results show that HRM-CenterNet improves accuracy as well as robustness, and meets the performance requirements of real-time edge detection.
Identifier
85124296772 (Scopus)
ISBN
[9781665442077]
Publication Title
Conference Proceedings IEEE International Conference on Systems Man and Cybernetics
External Full Text Location
https://doi.org/10.1109/SMC52423.2021.9658920
ISSN
1062922X
First Page
564
Last Page
569
Grant
61302163
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Zhang, Ke; Zhao, Kai; Guo, Xiwang; Feng, Xiaohan; and Tang, Ying, "HRM-CenterNet: A High-Resolution Real-time Fittings Detection Method.*" (2021). Faculty Publications. 4674.
https://digitalcommons.njit.edu/fac_pubs/4674