Transmission Line Inspection Image Recognition Technology Based on YOLOv2 Network With the gradual improvement of the intelligence of power grid inspection, it is of great significance to apply image recognition technology based on deep learning to the intelligent inspection of transmission lines. This paper proposes an image recognition technology for transmission line equipment based on YOLOv2 network. It utilizes the powerful learning ability and fast recognition speed of YOLOv2 network to accurately learn different characteristics of multiple devices under the condition that the background of transmission lines is varied, and quickly complete the image recognition task. After conducting thorough training on the image of the transmission line obtained by inspection, the accuracy of the YOLOv2 network reached 90.9%, and the recognition speed reached 30 frames per second. The simulation results show that the image recognition technology based on YOLOv2 network balances the recognition accuracy and speed well, and it satisfies the requirements of realtime intelligent inspection.

Meter Detection of Substation Scene Based on Deep Learning Automatic detection of the substation meters based on deep learning is of great importance. Meters are important equipment for monitoring substations safety. A large number of meters in the substation need to be detected and recognized, which is laborintensive and timeconsuming. Aiming at solving low detection accuracy problem in the existing methods, an efficient and accurate method based on YOLO for meter detection is proposed. First, we build a dataset of substation meters with 1432 images as the training set and 615 images as the test set. We use data augmentation strategy to solve the problem of data shortage and classimbalance. Second, according to the characteristics of the meter image, feature pyramid is used to train the model to effectively improve the recognition performance. Finally, we use the training warmup method to reduce the overfitting of the training model. Experiments results show that, compared with other methods, our method can achieve better performance on several benchmarks. The detection recall improves from 88.03% to 96.08% and mAP improves from 89.64% to 93.84% on the premise of ensuring realtime performance.