A Comprehensive Solution for Deep-Learning Based Cargo Inspection to Discriminate Goods in Containers
Abstract
In this work, we attempt to classify commodities in containers with HS(harmonized system) codes, which is a challenging task due to the large number of categories in HS codes and its hierarchical structure based on a product's composition and economic activity. To tackle this problem, in this paper we propose an ensemble model which incorporates fine-grained image categorization, data analysis on cargo manifests, and human-in-the-loop paradigm. By employing deep learning, we train a triplet network for fine-grained image categorization. Then, by investigating massive information from cargo manifests, unreasonable predictions can be filtered out. With human-in-the-loop embedded, human intelligence is integrated to justify the resulted HS codes. Moreover, a HS code semantic tree is built to trade off specificity and accuracy.
Cite
Text
Che et al. "A Comprehensive Solution for Deep-Learning Based Cargo Inspection to Discriminate Goods in Containers." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018. doi:10.1109/CVPRW.2018.00166Markdown
[Che et al. "A Comprehensive Solution for Deep-Learning Based Cargo Inspection to Discriminate Goods in Containers." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.](https://mlanthology.org/cvprw/2018/che2018cvprw-comprehensive/) doi:10.1109/CVPRW.2018.00166BibTeX
@inproceedings{che2018cvprw-comprehensive,
title = {{A Comprehensive Solution for Deep-Learning Based Cargo Inspection to Discriminate Goods in Containers}},
author = {Che, Jiahang and Xing, Yuxiang and Zhang, Li},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2018},
pages = {1206-1213},
doi = {10.1109/CVPRW.2018.00166},
url = {https://mlanthology.org/cvprw/2018/che2018cvprw-comprehensive/}
}