Circular ripples are surface defects in the manufacturing process of contact lenses, resulting from uneven distribution of hydrogel materials, causing a concentric contraction along the edge of the lens. These defects are challenging to detect in projection inspections, leading to poor product quality. Detecting circular ripple defects poses a technical challenge in the production of contact lenses. In this study, a circular ring illumination imaging system was constructed based on the characteristics of this defect. An image model database of circular ripple defects was collected, and a lightweight detection algorithm for contact lens circular ripple defects based on an improved RT-DETR was introduced. Initially, the original ResNet18 backbone of RT-DETR was modified by replacing the BasicBlock with a lightweight FasterNetBlock. Subsequently, the SimAM three-channel attention mechanism was integrated into the Neck part of RT-DETR to enhance the model’s accuracy. Finally, the GIoU loss function was replaced with the MPDIoU loss function to accelerate convergence and improve detection accuracy. Experimental results demonstrate that the improved RT-DETR algorithm achieved a mAP@0.5 of 94% on the contact lens circular ripple database, a 3.1% improvement over the original RT-DETR algorithm. Params and FLOPs were reduced by 15.6% and 13%, respectively, compared to the original algorithm. This algorithm effectively reduces computational complexity, enhancing the mean average precision of contact lens circular ripple defect detection. It holds promise for overcoming technical challenges in online detection of circular ripple defects in contact lenses.