To detect the micro gas leakage in petrochemical production, a single-frame small target detection method was proposed by using infrared images. The low-rank sparse decomposition theory and sparse representation theory were researched and an innovative method to detect a micro-target was proposed based on tensor low-rank decomposition and sparse representation. The tensor decomposition form was employed in exploiting the information contained in background matrices, The priori knowledge was used to construct a micro gas leakage target dictionary, meanwhile, the micro-gas leakage targets were decomposed by low-rank constraint in the background and sparse representation in the micro-target. Finally, the algorithm was solved optimally by using Inexact Augmented Lagrange Multiplier(IALM) method and its merits were compared with that of common methods. The results indicate that the proposed algorithm has better detection efficiency than that of common methods and it shows better ROC (Receiver Operating Characteristics)curves. It concludes that these results meet the requirements of micro gas leakage detection during industrial productions.