Electric transmission towers rely on insulators to prevent electrical current from leaking from high-voltage conductors to the ground. These insulators are critical to maintaining safe and efficient energy transmission. However, they are susceptible to various types of damage, including flashover, where current arcs across air gaps, and physical breakage caused by electrical stress or mechanical load. Early identification of such defects is essential for prioritizing maintenance, preventing energy loss, and avoiding damage to transmission infrastructure.

Power utilities routinely inspect transmission and distribution systems by capturing images of transmission assets using helicopters, drones, ground vehicles, or handheld equipment. These inspections can generate hundreds of images per mile, and with thousands of miles of lines to monitor, manually reviewing every image is extremely time-consuming and inefficient.

This deep learning model automates the detection of defects in insulators from high-resolution imagery. It significantly reduces the manual effort required, enabling faster, more accurate, and scalable inspection workflows that support proactive maintenance and enhance grid reliability.