Contact network insulator detection method based on reconstruction and classification convolution self-coding network
An insulator detection and convolutional self-encoding technology, applied in neural learning methods, biological neural network models, analysis materials, etc., can solve problems such as background interference, complex images, and complex image processing techniques, and improve robustness and robustness. The effect of stability, removal of background interference, and good decision-making reference
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[0124] collected by Figure 13 Take the image as an example, the details are as follows:
[0125] 1. Positioning segmentation. Such as Figure 14 As shown, using the Mask-RCNN convolutional neural network can accurately locate the insulator area and segment it from the background.
[0126] 2. Rotate the image. Such as Figure 15 As shown, the convex hull is searched by the Graham-Scan algorithm, and the minimum circumscribed rectangle is calculated for the insulator mask area to remove the background interference to the maximum extent. The inclination angle of the circumscribed moment is calculated according to the rectangular coordinates, the insulator is adjusted to be horizontal by coordinate transformation, and the image of the horizontal insulator is obtained.
[0127] 3. Outlier detection. Such as Figure 16 As shown, in order to avoid the interference of noise, outlier detection based on K-Nearest Neighbor (KNN, kNearest Neighbor) is used to remove noise.
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