CA-CentripetalNet: A novel anchor-free deep learning framework for
  hardhat wearing detection
Zhijian Liu Nian Cai Wensheng Ouyang Chengbin Zhang Nili Tian Han Wang
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.