Abstract
Robot reading energy meter devices automatically is a basic task for smart grid. Unfortunately, the predicted accuracy is influenced by environment dramatically. In order to solve the problem, we propose a method which can metric fog concentration in environment before meter reading. Moreover, based on the characteristics of image and fog influence, joint features and SVM are used to discriminate the concentration level of foggy image. Specially, Contrast feature and DFT feature are extracted as joint features to describe the foggy image and SVM is used to study and train the joint feature. Finally the fog concentration level is discriminated naturally. The experimental results demonstrate the effectiveness of proposed method in term of judging the meter image with five level.
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Tian, Z., Zhang, G., Cao, K., Liao, Y., Li, R. (2019). Fog Concentration Grade Judgment for Meter Reading Based on SVM. In: Milošević, D., Tang, Y., Zu, Q. (eds) Human Centered Computing. HCC 2019. Lecture Notes in Computer Science(), vol 11956. Springer, Cham. https://doi.org/10.1007/978-3-030-37429-7_39
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DOI: https://doi.org/10.1007/978-3-030-37429-7_39
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