Image segmentation has been commonly used for segmenting regions of interest in the industrial field. However, segmenting small objects in a reflective background is a complex task that limits the performance of image segmentation in the industry. We take residual cut tobacco in industrial production as an object and propose an automatic detection method. This method overcomes the influence of reflective background and achieves accurate detection of small-area residual cut tobacco. The proposed method combines multi-feature fusion method and Markov random field model to construct a coarse-to-fine segmentation framework. The coarse part is used to eliminate the effect of specular highlights and specular reflection. The fine part is used to detect and clear small regions of residual cut tobacco that are wrongly segmented by the coarse part. We collect 2411 images in the real industrial production line for evaluation. The experimental results show that the proposed method yields an averaged precision and mean intersection over union of 95.85% and 86.71%, which outperforms some detection methods based on deep learning.