Automatic hardhat wearing detection can strengthen the safety management in construction sites, which is still challenging due to complicated video surveillance scenes. To deal with the poor generalization of previous deep learning based methods, a novel anchor-free deep learning framework called CA-CentripetalNet is proposed for hardhat wearing detection. Two novel schemes are proposed to improve the feature extraction and utilization ability of CA-CentripetalNet, which are vertical-horizontal corner pooling and bounding constrained center attention. The former is designed to realize the comprehensive utilization of marginal features and internal features. The latter is designed to enforce the backbone to pay attention to internal features, which is only used during the training rather than during the detection. Experimental results indicate that the CA-CentripetalNet achieves better performance with the 86.63% mAP (mean Average Precision) with less memory consumption at a reasonable speed than the existing deep learning based methods, especially in case of small-scale hardhats and non-worn-hardhats.
@article{arxiv.2307.04103,
title = {CA-CentripetalNet: A novel anchor-free deep learning framework for hardhat wearing detection},
author = {Zhijian Liu and Nian Cai and Wensheng Ouyang and Chengbin Zhang and Nili Tian and Han Wang},
journal= {arXiv preprint arXiv:2307.04103},
year = {2023}
}
Comments
It has been accepted for the journal of Signal, Image and Video Processing, which is a complete version. It is noted that it has been deleted for future publishing