Research on fragment identification of a static explosion field based on DenseNet and attention mechanism

DOI:	10.11809/bqzbgcxb2023.02.039
Keywords:	static explosive field fragment; SSD; DenseNet; attention mechanism
Abstract:	In view of the difficulty in the identification of small fragments in fragmenting target image recognition in a static blasting field, this paper uses an improved SSD target detection algorithm to modify the backbone network in the SSD model to DenseNet, which reduces the network parameters, reduces the consumption of the input image feature information, and preserves the details of the target objects to the greatest extent. In addition, the attention mechanism model is introduced, which combines channel attention mechanism and spatial attention mechanism to obtain the weights of the feature layer channels and feature points so as to fully extract the feature information of small fragments. The experimental results show that detection accuracy of the proposed method for small fragments reaches 89.82%. Compared with the traditional SSD method, the false detection rate increases by 3.6% and the missed detection rate increases by 6%, which provides a guarantee for analyzing the flying characteristics and damage effect of the fragments.
Published:	2023-02-28
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