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<div align="center"> <h1> SiamFuseNet: A Pseudo-Siamese Network for Detritus Detection from Polarized Microscopic Images of River Sands </center> <br> <center> </h1> <p align="center"> Cong Wang¹, Shiping Ge¹, Zhiwei Jiang¹, Huizhen Hao^1,2, Qing Gu^1,* <br> ¹ State Key Laboratory for Novel Software Technology, Nanjing University <br> ² School of Information and Communication Engineering, Nanjing Institute of Technology <br> ^* Corresponding author <br><br> Computers & Geosciences<br> [<a href="https://doi.org/10.1016/j.cageo.2021.104912" target="_blank">paper</a>] <br> </div>

Abstract

Detecting detritus from the polarized microscopic images of river sands is the first step in the tasks of sediment source analysis, tectonic evolution, and lithofacies paleogeography. Traditional detritus detection mainly relies on professionals to identify and detect manually, which is both time-consuming and labor-intensive facing large volumes of microscopic images of river sands. Currently, deep learning techniques, including Convolutional Neural Network (CNN), have achieved good performance in many visual detection tasks, and can be applied to geological tasks such as detritus detection. In this paper, we propose a novel CNN-based pesudo-siamese network for detritus detection called the SiamFuseNet. SiamFuseNet accepts both plane-polarized and cross-polarized images as input, and learns the fused feature representation to improve the detection accuracy. Besides, Both the multi-scale detection structure and the loss function are optimized to improve both the performance and robustness of SiamFuseNet. Compared to available object detection models, the experiment results show that SiamFuseNet robustly achieves greater accuracy of detritus detection without sacrificing the detection speed.

Reference

@article{wang2021siamfusenet,
    title={SiamFuseNet: A pseudo-siamese network for detritus detection from polarized microscopic images of river sands},
    author={Wang, Cong and Ge, Shiping and Jiang, Zhiwei and Hao, Huizhen and Gu, Qing},
    journal={Computers \& Geosciences},
    volume={156},
    pages={104912},
    year={2021},
    publisher={Elsevier}
}