Development of an Objective Detection Method for Streaky Uneven Dyeing to Enable Automated Quality Inspection of Synthetic Fibers

Quality control of synthetic fibers currently depends primarily on the visual evaluation of streaky uneven dyeing in knitted fabrics by expert inspectors, a method limited by its lack of quantifiability and objectivity. As a quantitative alternative, a previously proposed method utilizing the mean gray-level gradient from image analysis proved effective for a limited set of samples; however, it failed to generalize to routine samples exhibiting diverse defects. To address this limitation, this study identified image features capable of objectively detecting uneven streaky dyeing in routine samples. An analysis of inspector markings revealed significant variability in human judgment while also indicating the importance of gray-level gradient patterns with relatively high contrast. By incorporating the latter with geometric characteristics, a new feature was defined: a region displaying a gray-level gradient value exceeding 25% of the upper limit in conjunction with an elongated shape characterized by an aspect ratio of at least 7.0. Validation experiments demonstrated that this feature achieved a high detection rate (recall) of 92.5% for inspector-identified instances. These findings establish a foundation for comprehensive evaluation based on the intensity, length, and number of individual streaks, representing a significant step toward fully automated quality control.