Content-Aware Network for Quality Estimation of Copper Scrap Granules

Authors: Kaikai Zhao, Zhaoxiang Liu, Kai Wang, Shiguo Lian

Conference: ICIC 2024 Posters, Tianjin, China, August 5-8, 2024

Pages: 16-27

Keywords: Copper scrap granules, Quality level, Visual transformer, Content-Aware

DOI: 10.65286/icic.v20i2.76059

Abstract

To determine the quality level of copper scrap granules, existing methods have to manually identify all kinds of impurities mixed in copper scrap granules relying on technicians’ experience. In this paper, we pioneer a computer vision-based approach called Content-Aware Network (CANet) to estimate the quality of copper scrap granules. Specifically, CANet consists of a visual transformer-based backbone that extracts the semantic features from copper scrap granule images, a multi-layer perception-based neck that explicitly estimates the volume proportion of copper to copper scrap granules and implicitly estimates the counterparts of varieties of impurities and a well-designed head that directly outputs the quality result. Benefiting from our novel architecture and loss functions, CANet can be trained in an end-to-end manner to accurately estimate the quality of copper scrap granules only with the binary annotated images (copper area and non-copper area) without identifying these unknown impurities and their densities in advance. Experiments on real copper scrap granule datasets demonstrate the effectiveness and superiority of our proposed method.

BibTeX Citation:

@inproceedings{ICIC2024,
    author = {Kaikai Zhao, Zhaoxiang Liu, Kai Wang, Shiguo Lian },
    title = {Content-Aware Network for Quality Estimation of Copper Scrap Granules },
    booktitle = {Proceedings of the 20th International Conference on Intelligent Computing (ICIC 2024)},
    month = {August},
    date = {5-8},
    year = {2024},
    address = {Tianjin, China},
    pages = {16-27},
    note = {Poster Volume Ⅱ}
    doi = {10.65286/icic.v20i2.76059}
}