"HRM-CenterNet: A High-Resolution Real-time Fittings Detection Method.*" by Ke Zhang, Kai Zhao et al.
 

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

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