We propose the bottom-up method for the perceptual grouping of parallel lines according to the sharp structure characteristics of insulator strings in the detection of transmission line defects of unmanned aerial vehicles (UAVs). This method is applied to improve the correct recognition rate of the insulator and to overcome the deficiency to the color-based insulator recognition method. First

the line segments extracted from all directions are divided into six groups in the inspection image. The line segments with approximate lengths

directions

and center point orientations are then grouped into parallel segment clusters. Insulator regions are detected by combining parallel segment clusters and organizing the circumscribed shapes of these clusters based on knowledge about transmission line models. Glass insulator defects can be diagnosed according to the similarities among the feature blocks of the mean and variance of inertia moment

the adaptive partition for insulator regions

and the calculation of the direction and distance between the insulator strings. Compared with the HSI color-based insulator recognition method

the insulator recognition method based on multiple internal parallel line structures exhibits more stable performance and is thus more suitable for transmission line inspection. Transmission line images from UAV inspection are tested

and results show that the proposed method can be used to identify various types of insulators and to detect insulator off-chip defects effectively in cluttered backgrounds.