1,736 research outputs found

    Uneven illumination surface defects inspection based on convolutional neural network

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    Surface defect inspection based on machine vision is often affected by uneven illumination. In order to improve the inspection rate of surface defects inspection under uneven illumination condition, this paper proposes a method for detecting surface image defects based on convolutional neural network, which is based on the adjustment of convolutional neural networks, training parameters, changing the structure of the network, to achieve the purpose of accurately identifying various defects. Experimental on defect inspection of copper strip and steel images shows that the convolutional neural network can automatically learn features without preprocessing the image, and correct identification of various types of image defects affected by uneven illumination, thus overcoming the drawbacks of traditional machine vision inspection methods under uneven illumination

    Evolution and control of the phase competition morphology in a manganite film

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    The competition among different phases in perovskite manganites is pronounced since their energies are very close under the interplay of charge, spin, orbital and lattice degrees of freedom. To reveal the roles of underlying interactions, many efforts have been devoted towards directly imaging phase transitions at microscopic scales. Here we show images of the charge-ordered insulator (COI) phase transition from a pure ferromagnetic metal with reducing field or increasing temperature in a strained phase-separated manganite film, using a home-built magnetic force microscope. Compared with the COI melting transition, this reverse transition is sharp, cooperative and martensitic-like with astonishingly unique yet diverse morphologies. The COI domains show variable-dimensional growth at different temperatures and their distribution can illustrate the delicate balance of the underlying interactions in manganites. Our findings also display how phase domain engineering is possible and how the phase competition can be tuned in a controllable manner.Comment: Published versio

    TLMCM Network for Medical Image Hierarchical Multi-Label Classification

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    Medical Image Hierarchical Multi-Label Classification (MI-HMC) is of paramount importance in modern healthcare, presenting two significant challenges: data imbalance and \textit{hierarchy constraint}. Existing solutions involve complex model architecture design or domain-specific preprocessing, demanding considerable expertise or effort in implementation. To address these limitations, this paper proposes Transfer Learning with Maximum Constraint Module (TLMCM) network for the MI-HMC task. The TLMCM network offers a novel approach to overcome the aforementioned challenges, outperforming existing methods based on the Area Under the Average Precision and Recall Curve(AU(PRC)AU\overline{(PRC)}) metric. In addition, this research proposes two novel accuracy metrics, EMREMR and HammingAccuracyHammingAccuracy, which have not been extensively explored in the context of the MI-HMC task. Experimental results demonstrate that the TLMCM network achieves high multi-label prediction accuracy(80%80\%-90%90\%) for MI-HMC tasks, making it a valuable contribution to healthcare domain applications
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