The performance of image processing is crucial for accurately sorting antimony ore, yet several challenges persist. Existing image segmentat
The performance of image processing is crucial for accurately sorting antimony ore, yet several challenges persist. Existing image segmentation methods struggle with X-ray ore images that contain high noise and interference. Additionally, traditional classification methods primarily utilize single physical properties, such as the R-value, leading to low accuracy. To address segmentation issues, this paper proposes an improved method based on concave detection. This involves obtaining a binary image of antimony ore through adaptive threshold segmentation, extracting the ore contour, and detecting concave points using advanced techniques. The influence of interfering concave points is minimized with the three-wire method, while noise points are reduced through morphological operations based on area calculations. This results in accurate segmentation of the adherent antimony ore. For classification, this paper introduces a training method that combines transfer learning with shallow partial initialization. Transfer learning is employed to mitigate the challenges of limited antimony ore datasets when using deep learning models. The pre-trained model is then partially re-initialized according to a tailored strategy. Finally, fine-tuning is performed on the antimony ore dataset to achieve optimal results. Experimental results show that the antimony ore segmentation method proposed in this paper achieves accurate segmentation (96.27% correct segmentation rate). The antimony ore classification model training method proposed in this paper can effectively release some redundant parameters of the pre-training model, and has better classification performance on the target dataset (86.76% accuracy). Both methods are superior to traditional methods. [ABSTRACT FROM AUTHOR]
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