Detection model for wearing status of safety helmet based on attention mechanism
To alleviate the problems of missing detection,false detection,and inaccurate localization on wearing status of safe-ty helmet in the scenario of work safety video surveillance due to small object size,complex background,and occlusion,a two-stage high-precision detection model for the wearing status of safety helmet based on the attention mechanism was proposed.A feature pyramid network with bidirectional multi-layer connected fusion was proposed,and the spatial attention mechanism based on encoder-decoder was designed to remove the redundant features,thereby enhancing the recall rate of small objects.The multi-scale convolution was used to extract the multi-layer context features of candidate region,and the attention mecha-nism was employed to explicitly weight the context features with different levels and different scales,thereby improving the ro-bustness of the model in complex background.The classification and localization networks of candidate region were decou-pled,and the channel attention and spatial attention were respectively introduced to enhance the classification and localization accuracy of the model.The research results indicate that the helmet wearing status detection model based on attention mecha-nisms is overall superior to the current related mainstream high-precision detection models such as YOLOv3、SSD、RetinaNet、Faster R-CNN and TridentNet.The research results can effectively mitigate the issues of missing detection,false detection,and inaccurate localization on the wearing status of safety helmet in the work safety video surveillance scenarios.
work safetydetection on wearing status of safety helmetobject detectionattention mechanismfeature pyra-mid
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