Abstract
In view of the problems of high missed detection rate and inaccurate position of small targets in the cigarette detection algorithm based on Faster Regions Convolutional Neural Networks(Faster R-CNN), a cigarette detection algorithm based on Feature pyramid networks (FPN) and Faster R-CNN is proposed. The feature map with high-level semantic information and low-resolution of the last layer is adopted by the Faster R-CNN as the input of Region Proposal Network (RPN), resulting in low recognition rate of small targets. The improved Faster R-CNN framework combined with FPN algorithm continuously fuses the high-level feature maps with the feature maps of the front layer through up-sampling, and constructs the feature pyramid model of different scales as the input of RPN network, which improves the detection effect of cigarette effectively.
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