Aiming at the challenges of the low reliability and poor practicability of current optical fiber coil defect detection approaches, an algorithm of optical fiber coil defect detection based on an enhanced low-rank representation model is suggested in this paper. Based on the low-rank representation theory, the defect detection challenge is modeled, defect-free optical fiber coil image is modeled as a low-rank structure, and defect is modeled as a sparse structure. Meanwhile, the Laplacian regularization constraint is incorporated into the low-rank representation model to widen the gap between defect and background. To enhance the efficiency of the algorithm, the idea of power iteration is employed to achieve singular value decomposition. The algorithm is confirmed via experiments, and the findings indicate that the suggested algorithm possesses a good detection performance for various defect types. Compared with other algorithms, the suggested algorithm attains the best performance.