Focusing on the ignorance of the image structure in Latent Dirichlet Allocation (LDA) model, a LDA fastener detection model integrated with image texture information, namely TS_LDA, was proposed. Firstly, a single-channel Local Binary Pattern (LBP) method was designed to acquire the image texture structure, and the texture information was treated as a label of visual word. The joint distribution of words and labels reflected the characteristics of an image structure. Secondly, those labels were embedded into LDA, and image topics were derived from words and labels. The improved distribution of topics considered the image structure. Finally, the classifier was trained and fastener states were identified on the basis of topic distribution. Compared with the LDA method, the differences between normal and disabled fasteners increased by 5%-35%, the average misdetection rate decreased by 1.8%-2.4% in the topic space of TS_LDA. The experimental results show that TL_LDA is able to enhance the accuracy of fastener image modeling, thus inspects fastener states more precisely.