Deep mining algorithms and multi-feature fusion algorithms based on local binary patterns are effective methods for extracting the fixture features of leaky cables in railway tunnels； however， there are disadvantages that the descriptors are not expressive enough and that their feature dimensions are too high. In this paper， layered continuous gradient local binary pattern （LCG-LBP） was proposed， which could realize the scale transformation of leaky cable fixture features. It could reduce the feature dimension of the fusion descriptor extracted from down-sampling feature maps. It could also improve the classification accuracy of faulty fixture images effectively. First， the improved algorithm based on center-symmetric local binary pattern （CS-LBP） and the adaptive threshold obtained by the global gray average value were used to calculate the gradient direction feature in a circle domain unit， and the complete preliminary gradient direction feature map was obtained in this way. Then， two consecutive down-sampling iterations were performed on this preliminary feature map to obtain two down-sampling feature maps， and the continuous gradient features were extracted from these two down-sampling feature maps. Finally， the two layers of continuous gradient features in different scales were connected in series as a fusion descriptor， and a support vector machine （SVM） was used to complete the defect detection process using faulty cable fixture images obtained from railway tunnels. The experimental results show that the recall and accuracy of the algorithm proposed in this paper are 0.923 and 0.857， respectively， which show that the proposed algorithm has obvious advantages compared with local binary pattern （LBP）， CS-LBP， and other variants.