MGCAN: A Novel Method for Visual Power Monitoring Systems

Imaging quality judgement provides useful meaning to benefit intelligent visual power systems. However, accurate identification of possible failures of the monitoring equipment remains challenging. This paper proposes a novel deep architecture to improve the assessment of abnormalities by considering a novel mutual-guided convolutional attention network (MGCAN) and a multi-region scheme. More specifically, the MGCAN exploits various adequate, low-level information to generate spatial and structural flows. Accordingly, it further extends attention mechanisms to model both inter-flow correlations and inter-channel relationships in the network design. The multi-region scheme features a spatial pyramid random-crop strategy and a region-fusion strategy to handle locally non-uniform characteristics among categories. In this way, the whole architecture provides an end-to-end and adaptive learning procedure relevant to quality perception that focuses on learning important features and mining discriminative regions. Experimental results demonstrate its superiority to prior methods for judging abnormalities. The proposed method can be easily extended to an entire surveillance system.