Small-scale equipment part detection method based on weak supervision collaborative learning in open scene of electric power field

Abstract

The invention discloses a small-scale equipment part detection method based on weak supervision collaborative learning in an electric power field open scene, and the method comprises the steps: carrying out the fusion of shallow features and deep features through employing a feature pyramid based on the features of a small target of an equipment part, and obtaining richer information. After the extracted multi-scale features are input into the candidate region generation network, candidate regions under different scale features can be generated, and the processing range of the strong and weaksupervised learning network is divided according to the scale sizes of the candidate regions, so that the high performance of the strong supervised sub-network and the collaboration of the weak supervised sub-network are brought into full play. The time cost is reduced to a great extent, and the efficiency and the precision are balanced. Meanwhile, a detection framework different from a classic Faster R-CNN model is used for detecting the target, and the precision and speed of small target detection are improved at the same time.

Description

technical field [0001] The invention discloses a small-scale equipment component detection method based on weak supervision collaborative learning in an open scene in the electric power field, and belongs to the technical field of smart grids. Background technique [0002] Electricity is one of the indispensable sources of energy in the production and life of human society. With the large-scale growth of transmission lines and the increasing number of transmission equipment, the safety inspection of equipment, especially the timely monitoring of equipment component defects, is becoming more and more important. At present, the inspection of power transmission equipment in my country mainly adopts traditional manual inspection and automatic inspection by drones, which have the following problems: heavy workload, low efficiency, and large lag in fault judgment. [0003] For this reason, research and development have been started in this technical field: using neural network lea...

AI Technical Summary

Problems solved by technology

At present, the inspection of power transmission equipment in my country mainly adopts traditional manual inspection and automatic inspection by drones, which have the following problems: heavy workload, low efficiency, and large lag in fault judgment

However, these two types of network models have their own advantages and disadvantages. The detection accuracy of the neural network model based on the candidate area is high, but the detection speed is slow; The number of grids has a great relationship. If you want to detect small targets, you often need to divide more grids, but it will cause the detection speed to drop rapidly.

Although the detection accuracy of the neural network model based on the candidate area is high, it often does not have a high accuracy guarantee for the detection of small targets.

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