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Automatic hardhat wearing detection can strengthen the safety management inconstruction sites, which is still challenging due to complicated videosurveillance scenes. To deal with the poor generalization of previous deeplearning based methods, a novel anchor-free deep learning framework calledCA-CentripetalNet is proposed for hardhat wearing detection. Two novel schemesare proposed to improve the feature extraction and utilization ability ofCA-CentripetalNet, which are vertical-horizontal corner pooling and boundingconstrained center attention. The former is designed to realize thecomprehensive utilization of marginal features and internal features. Thelatter is designed to enforce the backbone to pay attention to internalfeatures, which is only used during the training rather than during thedetection. Experimental results indicate that the CA-CentripetalNet achievesbetter performance with the 86.63% mAP (mean Average Precision) with lessmemory consumption at a reasonable speed than the existing deep learning basedmethods, especially in case of small-scale hardhats and non-worn-hardhats.

CA-CentripetalNet: A novel anchor-free deep learning framework for hardhat wearing detection