We present an efficient and automated method for the detection of bar structure in optical images of galaxies using a deep convolutional neural network. In our study we use a sample of 9346 galaxies in the redshift range 0.009-0.2 from the Sloan Digital Sky Survey, which has 3864 barred galaxies, the rest being unbarred. We reach a top precision of ~94 per cent in identifying bars in galaxies using the trained network. This accuracy matches the accuracy reached by human experts on the same data without additional information about the images. The method can be easily scaled to the much larger samples anticipated from various surveys.