ICE-YoloX: An Effective Face Mask Detection Method

Deep learning technologies such as YoloX have achieved impressive progress in face mask detection recently. However, the neck network used in YoloX network may lead to severe confounding effect in feature mapping due to the inherent defect of channel reduction in hybrid fusion, which affects its precise localization ability of mask-wearing targets. To tackle this issue, we present a new FPN network structure (ICE-FPN) based on channel-enhanced feature pyramid network (CE-FPN) in this paper, which can mitigate the YoloX network confounding effect while reducing the number of parameters and computational effort caused by CE-FPN. Experiments conducted on the WMD dataset show that the mAP0.5 of the model improves from 99.54% to 99.62% and the mAP0.75 improves from 89.47% to 91.35%. The ablation and comparison experiments demonstrate that the proposed ICE-YoloX has achieved superior performance over existing methods.