Inspecting the smoothness and durability of the exterior and interior surfaces of manufactured products such as large passenger aircraft requires utilizing manufacturing precision instruments. Manual techniques are mainly adopted in inspections, which are, however, much costly and have low efficiency. Moreover, manual operation is more prone to missing and false inspections. To cope with these issues, effective precision instruments have been needed to devise intelligent inspections for

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... le damages as necessary tools. When many inspection tasks are investigated, very few public datasets are available to recognize the intelligent inspection of precision instruments. On the other hand, precision instruments can be applied to a wide variety of damages. YOLO V3 was proposed based on acquiring and processing the image appearances, which deal with the surface inspection of the precision instrument in this study. The image dataset of the impalpable damage was initially established. More specifically, the YOLO V3 detection network is leveraged to roughly calculate the location of the damage appearances and identify the damage type. Afterward, the designed level set algorithm was employed to obtain more accurate damage locations in the image block utilizing the characteristics of different types of damages. Finally, a quantitative analysis was performed employing the refined detection results. A deep architecture that can intelligently conduct damage detection was proposed. Besides, the proposed method exhibited a strong tolerance for unknown types of damages and excellent flexibility and adaptability. Extensive experimental results demonstrated that the proposed method can alleviate the shortcomings of the traditional inspection methods. Thus, it provided technical guidance for the applications of the intelligent orbital inspection of the robots. The proposed method with a more precise and noninvasive inspection technique remarkably accelerates the inspection time of impalpable damages on surfaces.