Abstract：Aiming at the shortcomings of existing infrared images, such as low resolution and poor definition, which can easily affect the fault detection effect of electrical equipment based on infrared images, a super resolution fault identification method of infrared images of electrical equipment based on multi-scale collaboration model is proposed. Firstly, a super resolution reconstruction network for infrared images of electrical equipment is constructed based on multi-scale collaboration model. The network is based on generative adversarial network. By introducing multi-scale collaboration module and two-channel structure, the adaptability of the super resolution reconstruction network to infrared images is improved, and the effect of image feature extraction is optimized. On the basis of infrared image super-resolution reconstruction, combined with deep learning target detection method, a super-resolution fault identification model of infrared image of electrical equipment is established. Experimental verification of the proposed method is carried out. The experimental results show that the infrared image quality can be significantly improved after the proposed super resolution reconstruction network, and the PSNR and SSIM values can be increased to 27.26dB and 0.828 3 respectively. The proposed infrared image super-resolution fault identification model can significantly improve the fault identification effect, mAP, mAR, mAC and mAIOU increased by 19.34%、19.14%、11.83% and 25.03% on average.
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2021, Vol. 36 
