A Defective Insulator Identification Method Based on Yolov3 Network and Particle Filter Algorithm
A particle filter algorithm and insulator identification technology, applied in neural learning methods, biological neural network models, calculations, etc., can solve problems such as various defect types, staying in the insulator identification stage, and high robustness
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[0091] A defect identification method based on YOLOv3 network and particle filter algorithm, the flow chart of the defect identification system is as follows figure 1 shown, including the following steps:
[0092] 1) Create sample sets and label files
[0093] 1.1) Sample collection: image collection of insulators in the real transmission line environment; the images are randomly collected in different regions, at different times, under different lighting conditions, and at different angles.
[0094] 1.2) The size of the insulator image is converted to 2048×2048, and the sample set composed of the insulator image is randomly divided into a training set and a verification set according to a certain ratio (80%, 20%); the training set is used to establish the required YOLOv3 network model, The validation set is used to test the performance of the trained model.
[0095] 1.3) Expand the number of samples through data enhancement methods; data enhancement methods include rotation...
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