X-ray Cigarette Detection Based on Improved YOLOv5
In order to solve the problems of low prediction accuracy and strong subjectivity in X-ray cigarette detection,this paper introduced an X-ray cigarette detection method based on improved YOLOv5.In this method,the Convolutional Block Attention Module(CBAM)is added into the backbone network of YOLOv5.CBAM is a simple and effective attention module for feedforward con-volutional neural networks that is able to sequentially infer attention maps along two separate dimen-sions,namely channel and space.Then adaptive feature refinement is performed by multiplying the attention map by the input feature map.Finally,the method is applied to the test data of cigarette X-ray in certain area.The experimental results show that the model has better prediction accuracy and provides a new idea for cigarette X-ray detection.
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