In the study of UAV detection of transmission line defects, in order to improve the accuracy of detecting insulator defects, a self-explosion insulator detection method based on optimized YOLOv5 is proposed. In the training, the activation function is optimized, and the original ReLU activation function is optimized to SiLU activation function. Through the training and verification of a large number of transmission line image data collected by UAV inspection, the experimental results show that this method can effectively detect the self-explosion and falling defects of glass insulators under various complex background conditions. The mean average accuracy is used to evaluate the method. The detection rate of insulator self-explosion and flake defect is 94.7 %.
Pipeline Magnetic Flux Leakage (MFL) inspection data processing is a necessary part of the pipeline internal detection tasks, which results are the bases of excavation and maintenance. At present, pipeline construction is increasing rapidly. Thus, pipeline inspection and maintenance work will increase year by year. In order to improve the work efficiency and quality, detection data processing should be done automatically or intelligently by computer instead of artificial way. Considering that MFL detection is still the mainstream of long-distance oil and gas pipeline inspection method, some processing technology for MFL data, such as pre-processing, data visualization, MFL image recognition and defect qualification, etc., is summarized, and its difficulties and hotspots are discussed.
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