A dangerous climbing behavior recognition algorithm in electric power field operation comprises the following steps that S1, a yov3 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, collecting picture data of a to-be-detected electric power field through an electric power field monitoring video or field photographing, marking a helmet part in the picture data through a label, and then making the picture data 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 climbing behavior recognition algorithm in the electric power field operation is high in recognition rate and excellent in recognition efficiency, dangerous climbing behaviors of workers can be found in time, and unsafe factors caused by the dangerous climbing behaviors are restrained.
Dangerous climbing behavior recognition algorithm in electric power field operation