基于RFB网络的特征融合管制物品检测算法研究

doi:10.16088/j.issn.1001-6600.2020080902

Abstract

Abstract: In order to improve the detection accuracy of controlled items, a target detection algorithm that combines Receptive Field Block (RFB) network structure and feature fusion is proposed. First, the collected security inspection data are eliminated and filtered, then, the security inspection data are manually labeled and the data are enhanced. Based on the MobileNetV3-SSD algorithm, the RFB network is introduced to improve its network structure to strengthen the network’s feature extraction capabilities, and the feature fusion method is used to improve the model’s small target detection capability. Finally, a security inspection dataset SCCI2020 is constructed to verify the performance of the algorithm, where the dataset contains 91 767 images. Experimental results show that the detection accuracy of this algorithm on the security inspection dataset SCCI2020 is 87.0%, which is 2.7 percentage point higher than the detection accuracy of the MobileNetV3-SSD algorithm. The detection accuracy on the COCO2014 general dataset and COCO2017 general dataset are 21.9% and 23%. Compared with VGG16-SSD and MobileNetV3-SSD algorithm, the detection accuracy is improved to a certain extent.

Key words: controlled items detection, small target, RFB network, feature fusion, MobileNetV3-SSD

CLC Number:

TP391.41

ZHANG Weibin, WU Jun, YI Jianbing. Research on Feature Fusion Controlled Items Detection Algorithm Based on RFB Network[J].Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(4): 34-46.

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Research on Feature Fusion Controlled Items Detection Algorithm Based on RFB Network

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