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We report an investigation of the impact of surface passivation on the optical properties of ZnO nanorods. Al2O3 coating and hydrogen plasma treatment were used to passivate the surface states. It was found that Al2O3 coating led to the suppression of the deep level emissions, while hydrogen plasma treatment completely quenched the deep level emissions. It was confirmed that the surface states of the as-grown ZnO nanorod arrays indeed contributed to the deep level emissions. Evidence was also provided that shows surface states have a greater impact on the green emission than the orange emission and may cause the negative thermal quenching behavior. Moreover, the passivation effect was confirmed by the changes of the O 1s and Zn 2p spectra.

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Insulator faults detection is an important task for high-voltage transmission line inspection. However, current methods often suffer from the lack of accuracy and robustness. Moreover, these methods can only detect one fault in the insulator string, but cannot detect a multi-fault. In this paper, a novel method is proposed for insulator one fault and multi-fault detection in UAV-based aerial images, the backgrounds of which usually contain much complex interference. The shapes of the insulators also vary obviously due to the changes in filming angle and distance. To reduce the impact of complex interference on insulator faults detection, we make full use of the deep neural network to distinguish between insulators and background interference. First of all, plenty of insulator aerial images with manually labelled ground-truth are collected to construct a standard insulator detection dataset ‘InST_detection’. Secondly, a new convolutional network is proposed to obtain accurate insulator string positions in the aerial image. Finally, a novel fault detection method is proposed that can detect both insulator one fault and multi-fault in aerial images. Experimental results on a large number of aerial images show that our proposed method is more effective and efficient than the state-of-the-art insulator fault detection methods.