DRE-FCM: A Dark-Region Enhanced Fuzzy C-Means Algorithm for Vision-Based Weld Bead Inspection

As the automation of welding processes expands, vision-based inspection technologies for quantitative weld quality evaluation and defect detection have become increasingly important. However, the stable segmentation of weld bead regions remains challenging because actual weld images often include low contrast between the base material and weld bead, non-uniform illumination, spatter, and noise. To address these issues, this study proposes the Dark-Region Enhanced Fuzzy C-Means (DRE-FCM) algorithm, an FCM clustering-based weld bead segmentation method suitable for factory automation environments, using weld product images acquired under controlled experimental conditions. The segmented weld bead regions are subsequently classified into good and defective products using a modified projection-based classification method. The proposed method employs Firefly Algorithm-optimized Fuzzy C-means (FAFCM) to alleviate sensitivity to initial cluster centers and reduce the tendency to converge to local optima. In the preprocessing stage, the Inverse Gaussian Gradient (IGG) is applied to enhance weak edges in dark regions and compensate for ambiguous boundaries caused by brightness imbalance. An iterative noise reduction filter is further applied to reduce the noise sensitivity of FAFCM and improve weld bead object extraction. The extracted objects are then classified using a modified IHP-Seg. algorithm based on morphological information, including weld bead length and width. Experimental results show that the proposed method achieved a mean Intersection over Union (mIoU) of 0.94, a mean Pixel Accuracy (mPA) of 0.98, and an average segmentation time of 0.15 s, outperforming the compared conventional and deep learning-based methods. Furthermore, all objects were correctly classified, demonstrating the applicability of the proposed method to weld quality inspection in limited data environments.