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The detection of underground pipeline safety in various cities has been a hot and difficult issue. Traditional inspection instruments are not only time-consuming and laborious, but also have a high rate of false detection. With the development of technology, computer vision related methods are also used in pipeline inspection, but the speed and effect of detection are not satisfactory. In view of the complexity and high cost of the traditional detection methods, a content based SIFT+LSH algorithm for pipeline defect image retrieval is proposed. This method first selects SIFT features more obvious advantages, make full use of the characteristics of pipeline defect image, select the LSH algorithm to optimize the image SIFT feature, it is transformed into Hash encoding, and improve the retrieval speed. The experimental results show that the retrieval method of pipeline SIFT features and LSH algorithm based on the defects, compared with the traditional SIFT feature retrieval method and Euclidean distance, the retrieval speed is greatly improved, so the detection personnel can quickly get satisfactory retrieval results in the actual operation.