A STUDY ON AUTOMATIC IDENTIFICATION AND COUNTING FREQUENCY ANALYSIS OF TRANSMISSION LINE DANCING FEATURES BASED ON DEEP LEARNING

Transmission line galloping—wind-induced conductor vibrations—threaten grid safety by damaging components and triggering outages. An AI-driven method using HD cameras captures galloping video, which is frame-segmented via DirectShow and clarified with adaptive gamma correction. A Markov random field model isolates conductors/spacers (foreground) from backgrounds, and DeepLabv3+ extracts spatial features for displacement/angle quantification and frequency analysis. Experimental results confirm robust preprocessing, accurate component segmentation, and reliable galloping pattern detection, enabling proactive maintenance and grid resilience. Keywords: Transmission line; Deep learning; Neural network model; Dance characteristics; Counting frequency; Streaming media; Gamma correction; Convolutional layer; Video image segmentation; MRF; Edge point; CBMA