To address the need for large-scale drilling of acoustic holes in aero-engine nacelle composite liners， as well as challenges posed by complex material surfaces and the small size and high density of holes， a visual detection system was developed for a robotic multi-spindle drilling system. To overcome the lack of labeled data for composite acoustic holes， a semi-supervised segmentation method was introduced for precise segmentation. Based on these results， a reference hole detection scheme was designed using a geometric parameter fitting algorithm to accurately identify reference holes prior to drilling. Porosity detection was achieved using a porosity calculation formula combined with segmentation outcomes. A visual detection system， integrating LabVIEW and Python， was developed to automate the detection of acoustic hole porosity and reference holes.Tests on composite liner samples demonstrated that the improved semi-supervised method reduces labeled data requirements by 70% while achieving an mIoU of 95.70%. Training and detection parameters were significantly reduced. The visual detection system， incorporating the semi-supervised method， achieved a porosity detection variance of only 0.023% compared to manual results and demonstrated higher accuracy in reference hole detection.The visual detection system satisfies the high-quality manufacturing standards of aero-engine nacelles and meets efficiency demands for drilling and detection. The integration of semi-supervised methods into the system is a significant advancement for multi-spindle robotic drilling in scenarios with limited labeled data.