A detection method integrating visual saliency and group decision making was proposed with the aim of achieving efficient and high-precision detection of hole features in aircraft parts in complex environments. First， an image enhancement step was incorporated into the classical frequency-tuned （FT） saliency detection algorithm， and each pixel was assigned a weight based on the center of the maximum saliency region. This improved method was used for hole region segmentation. Second， a novel mathematical morphological edge detection algorithm with multi-scale and multi-structural elements was designed. This algorithm was combined with contour thinning to extract hole contours. Finally， the centroid positions of the contour points were obtained using the Meanshift algorithm and a new model based on group decision making was established for calculating the hole radius， thus obtaining key geometric parameters of the hole features. The results show that the improved visual saliency feature detection algorithm generates higher-resolution saliency maps that highlight hole features more prominently. This novel mathematical morphological edge detection algorithm obtains simplified and reliable contour points. It also exhibits a high robustness under complex conditions， including uneven lighting， various types of hole defects， and interference from the hole interior. This method can still perform hole detection successfully even with a noise density of up to 30%， and the errors in the coordinates of the center and the radius are less than 0.012 mm. The average detection time is only 0.236 s. It can accurately and robustly detect hole features in aircraft parts in complex environments.