For surface defect detection of carbon fiber reinforced polymer (CFRP) tow placement, where line-structured light images suffer from high-intensity noise regions and difficulties in extracting light stripe centers at fine gaps, this paper proposes an adaptive fast line-structured light center extraction algorithm. The method first reduces image complexity through preprocessing and light stripe ROI extraction. Subsequently, a contour tracking algorithm eliminates high-intensity noise and determines light stripe boundaries. Finally, an 8th-order filter convolution operator extracts skeleton points, combined with a normal centroid method to calculate sub-pixel center coordinates. When processing multiple defective tow placement images, our algorithm achieves optimal point cloud reconstruction accuracy. It requires only 5.36 s to extract center lines from 50 images, with a standard deviation of merely 0.38 pixel under extreme lighting conditions. Experimental results demonstrate that the proposed algorithm maintains accurate light stripe center extraction despite high-intensity noise and complex defect interference, exhibiting high precision, efficiency, and robustness. This provides reliable technical support for automated defect detection in composite material layup processes.