A dangerous border crossing behavior recognition algorithm in electric power field operation comprises the following steps that S1, a yolov3 is built through a python 3.6.5 + Keras engineering environment, backbone uses Darknet53, a loss function uses a cross entropy loss function, and pre-training parameters are loaded by using transfer learning; S2, picture data of a to-be-detected electric power field is obtained through an electric power field monitoring video or field photographing, a helmet part in the picture data is labelled through a labelimg, and then the picture data is made into a training set which can be fed into a network; S3, an open source data set is obtained, the open source data set and the training set are combined into a total data set, and the total data set is expanded in a data enhancement mode; S4, after the total data set is fed into the network, an Adam optimizer is used for optimization, the total data set is used for training, and a final model is obtained; and S5, detecting the safety helmet in real-time electric power field operation through the model. The dangerous border-crossing behavior identification algorithm in the electric power field operation is high in identification rate and excellent in identification efficiency, and can timely discover dangerous border-crossing behaviors of workers and suppress unsafe factors caused by the dangerous border-crossing behaviors.
Dangerous border crossing behavior recognition algorithm in electric power field operation