Abstract: To solve the issues of high labor intensity, significant safety risks, and strong subjective bias associated with traditional manual inspections of overhead power transmission and distribution lines in coal mines, this study analyzes the inspection content and anomaly characteristics of these lines. It establishes a positioning coordinate and path planning algorithm for drone inspections and proposes a solution for an autonomous drone inspection system. This system enables autonomous drone inspections, self-directed path planning, autonomous obstacle avoidance, and intelligent fault diagnosis. The drone uses its onboard radar to detect and identify obstacles, collects real-time image data of the transmission and distribution lines during flight, and performs intelligent analysis. A health indicator evaluation mechanism for overhead transmission and distribution lines is established to accurately identify line faults. Compared to traditional manual inspections, the adoption of autonomous drone inspection technology allows for the rapid completion of inspection tasks, precise fault location, and a reduction in labor intensity. The number of on-site inspection personnel is reduced from 5～6 to 1, improving operational and maintenance efficiency, lowering labor costs, and ensuring the stable operation of the transmission and distribution lines.