关键词: 瓷砖色差检测, 深度学习, 多任务学习, 色差分级, 智能检测系统

Abstract: To address the instability and subjectivity inherent in traditional tile color difference detection methods, a method integrating texture and color features was proposed for quantitative assessment and grading of chromatic aberration in tiles. A large-scale dataset named TILE-TCR (TILE Texture and Color Recognition), annotated with both texture and color labels, was constructed to enhance the model’s ability to represent these features. Additionally, the TILE-CAG (TILE Chromatic Aberration Grade) dataset was established to support the chromatic aberration classification task. Based on these datasets, the Fractal Encoding Network (FENet) was improved by incorporating Spatial Pyramid Pooling (SPP) and Squeeze-and-Excitation (SE) modules, enabling multi-task feature extraction and focusing on critical regions. A clustering algorithm was employed to adaptively determine the thresholds for chromatic aberration grading, allowing for objective quantification. Experimental results showed that the improved model achieved an accuracy oof 92.82% in the tile texture classification task, representing a 1.84 percentage point improvement over the baseline model. In the chromatic aberration grading task, all major evaluation metrics exceeded 90%. Furthermore, a simulated production line was built for industrial deployment and real-time performance testing. On commonly used tile specifications, the average detection time was under 3 seconds, meeting the real-time requirements for online inspection on industrial conveyor belts.

Key words: tile color difference detection, deep learning, multi-task learning, color difference grading, intelligent detection system