Wenbo Zhu1, Neng Liu1, Zhengjun Zhu2,*, Haibing Li1, Weijie Fu1, Zhongbo Zhang1, Xinghao Zhang1
Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 259-273, 2023, DOI:10.32604/iasc.2023.041860
Abstract The detection of ash content in coal slime flotation tailings using deep learning can be hindered by various factors such as foam, impurities, and changing lighting conditions that disrupt the collection of tailings images. To address this challenge, we present a method for ash content detection in coal slime flotation tailings. This method utilizes chromatographic filter paper sampling and a multi-scale residual network, which we refer to as MRCN. Initially, tailings are sampled using chromatographic filter paper to obtain static tailings images, effectively isolating interference factors at the flotation site. Subsequently, the MRCN, consisting of a multi-scale residual network, is… More >
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