Synthetical application of multi-feature map detection and multi-branch convolution

Two methods for improving the detection performance of neural networks are introduced in this paper, multi-feature map detection and multi-branch convolution structure. The former is to analyze the features of each convolution layer in the network separately, because these features have different resolutions and correspond to objects of different sizes. Finally, the comprehensive judgment of the analysis results can give better consideration to the overall situation and improve the accuracy of detection. The multi-branch convolution structure uses convolutions of different sizes on multiple branches to process input in parallel, and these branches are independent of each other. Finally, the feature maps corresponding to different receptive fields from each branch are combined and analyzed comprehensively. In this paper, the application process of the above two methods is described in combination with classical neural networks, such as the single shot multibox detector (SSD) and receptive field block (RFB) net.

Abstract licence: CC BY-NC-ND

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