The number of fabric defect classes is large and a single method is only effective for specific types of defects. In order to detect defects more effectively, a novel fabric defect detection algorithm is presented based on combining wavelet multi-scale product and mathematical morphology. After the dyadic wavelet transform on the images, mathematical morphology operations are utilized to the low frequency sub-image to obtain defect shape features. Then, the wavelet multi-scale product methods are used to the high frequency sub-images to realize suppressing noise and enhance defect edge linear features. Finally, the results above are fused to get the final result. Experimental results show that the false alarm rate and computational time are low, and the detection rate is high. The comprehensive performance is superior to the Gabor and wavelet algorithms.