The invention discloses a
power transmission line insulator
severe weather defect detection method based on a YOLOv10-MPM
algorithm, and the method comprises the steps: designing different
image enhancement strategies to simulate a natural scene based on an original visible light image, and constructing a
synthetic data set containing three typical complex weather scenes, namely
fog, rain and
snow; sequentially dividing the
synthetic data set into a
training set, a
verification set and a
test set according to a proportion, and dividing
data set labels into flashover, defect and insulator according to defect types; a designed multi-shape coordination attention mechanism MSSA is used for replacing an original point mode space attention module PSA in the YOLOv10n model; a C2f module is improved by using an MOCAA module; pIOU2 is adopted to replace a traditional
loss function; carrying out lightweight design on the model by using LAMP
pruning; therefore, a YOLOv10-MPM model is constructed; and training the YOLOv10-MPM model based on the
training set, and detecting the model by the
verification set. According to the method, the detection precision is improved, and the size of the
algorithm model is greatly compressed, so that the
algorithm model can be conveniently deployed on edge equipment such as an unmanned aerial vehicle.