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In order to improve the detection accuracy and real-time performance of the product appearance quality, we present a method of machine vision on the basis of feature fusion and multi-scale sliding window. In the training phase, in the first part of the paper, the HOG feature and Lab color feature of the image are extracted, and then the feature are fused using the classical correlation analysis(CCA). Next, support vector machine(SVM) is used to train the fused features to generate a classifier. In the detection phase, the accuracy requirements of different regions of the product are different. In order to improve the detection efficiency, the sliding window of different scales is generated. In each window, the feature extraction and feature fusion are carried out. Finally, the acquisition of the image sequence to match, to achieve real-time detection of product appearance scratches. In the experiment, different feature extraction methods are selected to compare and generate sliding windows with different sizes respectively. By analyzing the experimental results and combining the detection time and precision, the window scales of each region are determined. Experiments show that compared with the traditional detection method, the proposed method has a significant improvement in detection accuracy and real-time.