Abstract: The consequences of corrosion of the power distribution cabinet may result in poor contact, which may even lead to fire and explosion of some electrical control equipment. To this end, this paper proposes a neural network based multi-scale meter rust spot detection method. Firstly, based on a large number of rust spot data, a convolutional neural network (CNN) model for identifying rust spots is trained. Secondly, the trained CNN model is used to detect the position of the meter box, and real-time recognition of the rust spot on the surface of the meter is realized. The algorithm combines the multi-scale feature mapping through the cascaded RPN network, and makes full use of the location information of the low-level features and the strong semantic information of the high-level features to enhance the detection effect. For the collected rust spot dataset of the meter, the meter detection reaches 94.9% precision, which is better than 91.1% precision achieved by YOLOv2, and the rust spot classification precision reaches 94.5%. The recognition rate, robustness, real-time and stability of rust spot recognition can better meet the needs of practical applications.

Key words:  multi-scale features, cascaded RPN, rust spot detection