Detection of the damage to the pipeline insulation layer in a complex environment is challenging because the current automatic damage detection ignores the depth information and only uses the 2D image information. For the orbital robot inspection scene, a damage detection method for the pipeline insulation layer based on line structured light and YOLOv5 is proposed as a solution to this issue. After pre-segmenting the laser domain, the line structured light was added to the video acquisition device, and the laser center line was extracted using the adaptive threshold method. Further, the active ranging was operated in conjunction with the theory of line structured light depth measurement. To address the registration issue between the RGB images and depth information, RGB-D images were automatically created from the video by image stitching. Finally, RGB-D damage detection using the YOLOv5 algorithm with middle-level feature fusion was conducted to identify and classify two types of damages: bulges and dents. Experimental results indicate that the suggested method can extract RGB-D data from the captured video using the orbital robot, and the mean average precision of detection reaches 85.1%, making it possible to detect damage to the thermal insulation layer of the thermal pipeline with high accuracy and efficiency.