Improved Retina Net-based detection of mask specification wear
During the global spread of COVID-19,regulating the wearing of masks is the most effective form of prevention.When detecting whether masks are worn properly by dense groups of people in public places,there are problems of low detection accuracy and high rates of misde-tection and omission due to the close proximity of targets,occlusion and the presence of a large number of small targets.In order to solve the above problems,this paper proposes a mask speci-fication wearing detection method based on improved RetinaNet model.The introduction of the ECA-Net Attention Module makes it possible to give more attention to the mask target features and improve the detection accuracy;Secondly,the adaptive spatial feature fusion module ASFF is introduced after the feature pyramid FPN to make full use of the multi-scale features so that they can be more fully fused.Experiments using the method proposed in this paper on a home-made mask specification wearing dataset show that the overall performance of the method in this paper outperforms other detection algorithms.
NETL
NSTL
万方数据
