The work aims to analyze the characteristics of image spatial distribution and gray distribution, improve the region growing segmentation method, and solve the problems of long time consumption and poor effect of cable image segmentation caused by uneven illumination and various adverse factors on the tunnel wall. Firstly, the images were classified according to the adverse conditions of the wall. Then, the gray distribution characteristics of various images were analyzed by means of vertical projection of mean gray. After that, the cable ROI was obtained with the help of the envelope fitting deviation, and the seed point initialization and termination criteria were set in combination with the spatial distribution information of ROI, so as to greatly reduce the amount of data to be processed and avoid the influence of uneven illumination and adverse factors of the wall. Finally, comparisons were made with K-means clustering, global region growth and Unet semantic segmentation. For images of 1 000×1 800, the average segmentation time was about 0.42 s. For all kinds of adverse situations, the maximum false detection rate and missed detection rate were only 4%.The proposed method effectively overcomes the drawbacks of poor segmentation effect and longtime consumption of region growing method, and efficiently and successfully realizes accurate cable segmentation under the influence of non-uniform illumination and various adverse factors.