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Judging imaging quality is an important part of the maintenance of visual intelligent monitoring systems for electrical power scenes. However, accurate and efficient identification of possible abnormalities in imaging quality remains challenging. This paper proposes a novel multi-region two-stream deep architecture to improve judging abnormalities. The proposed architecture incorporates two-stream scheme and multi-region strategy to identify relevant information and explore hidden details. More specifically, in addition to color and intensity in the original images, the two-stream scheme uses high-frequency structure information from gradient images to enhance its performance. The multi-region strategy employs spatial pyramid random cropping and region fusion to handle locally non-uniform changes among categories: spatial pyramid random cropping characterizes images at different spatial pyramid levels, while region fusion focuses attention on cropped regions relevant to quality perception by using adaptive learning weights in a fully connected layer. In this way, the proposed strategy guides the framework to adequately and adaptively explore the discriminative regions hidden in the input images, and provides an end-to-end learning procedure. Experimental results demonstrate its strong performance for judging abnormalities, and the proposed method can be easily extended to the entire surveillance system.