An Improved YOLOv3 Target Detection Method for High Voltage Power Equipment Based on SGD and Cosine Annealing Algorithm
Aiming at the problems of poor real-time performance,low accuracy and difficult deployment in mobile terminal of the existing high-voltage power equipment detection methods,stochastic gradient descent(SGD)and cosine annealing algorithms are proposed to improve the YOLOv3 security detection algorithm for high-voltage pow-er transmission equipment.Darknet53 was used as the feature extraction network of the model,which improves the detection speed of the traditional detection method of high voltage power equipment.Then,the SGD optimization algorithm and cosine annealing algorithm are used to improve the safety detection accuracy of high-voltage power equipment and accelerate the convergence speed of the model in the early stage.Finally,the collected high-volt-age power equipment data set is used to train the entire network is trained.The results show that the average detec-tion accuracy of the YOLOv3 model on the high-voltage power equipment dataset reaches 97.08％,the detection speed reaches 56 frames/s,and the false detection rate is only 0.78％.
high voltage power equipmentYOLOv3Darknet53SGDcosine annealing algorithm
万方数据
维普
