The paper aims to propose an enhanced recognition method for low contrast defects based on the HSV color space to solve the problem that the low detection accuracy of low contrast printing defects in the current printing defect detection system. Firstly, the standard sample image and the printed image to be detected were converted from the RGB color space to the HSV color space, and the brightness component V, which was sensitive to visual changes, was extracted as the object to be detected; secondly, the Contrast Limited Adaptive Histogram Equalization (CLAHE) was combined with mathematical morphology to enhance the appearance of defects in the image to be detected; thirdly, the Connected Component Analysis (CCA) was used to obtain the five kinds of characteristic information of area, circumference, eccentricity, length-width ratio and circularity to establish 15 feature models on this basis. Finally, a printing defects recognition network based on PNN was constructed, and the recognition of low contrast printing defects was realized in Matlab. The average time of the 15 models was 475 ms, all of which were controlled at the millisecond level, meeting the real-time requirements of modern printing defect detection. Among them, the test accuracy of model 2 was 95%, which can identify spot defects; the test accuracy of Model 3 and Model 12 was 93% and 93.3% respectively, which can identify line defects; the test accuracy of model 5 was 93.1%, and it can identify surface defects. Moreover, the test accuracy was higher than that of the defect recognition method based on BP neural network. In terms of real-time and accuracy of defect detection, this method can detect low contrast printing defects in real time and accurately.