棉花杂质检测方法对于提高织物质量和降低生产成本具有重要意义。针对工业环境中非均匀光照条件下的棉花图像设计基于Gabor滤波器的杂质检测算法，依据Otsu法和形态学滤波将图像分割为前景区、背景区和交界区，然后在图像前景和背景区域内分别使用Gabor滤波器提取图像的纹理特征。设计一种针对Gabor滤波输出的自适应阈值分割算法，结合形态学滤波和连通域分析检测出棉花中的杂质。实验结果表明，本文算法有效地消除了由于光照条件造成的干扰，可以精确地检测出棉花中常见的各种杂质。

Cotton impurity inspection is important to control the quality of fabric and reduce production costs. To detect cotton impurity in industrial environment with uneven illumination, an impurity detection algorithm based on Gabor filter is proposed. In the algorithm, the image is divided into multiple zones using Otsu’s threshold method and morphological filter, and the texture features for foreground and background are then extracted using Gabor filter. An adaptive threshold segmentation method is designed and applied to the Gabor filter output, and then the impurities in the image are detected using a morphological filter and connected-zone analysis. Experiment results show that the proposed algorithm can remove the undesired interference caused by light source fluctuations effectively, and the common impurities in cotton also can be accurately detected.