ATENARY is an important part of the traction power supply system in high­speed railways, and it consists of some catenary support components (CSCs), such as Insulator, Rotary double ear, Brace sleeve. Fig. 1 shows the positions of all the crucial CSCs in the catenary system. The normal state of CSCs is the basis of the normal operation of high­speed trains. However, fault states of CSCs could occur due to the vibration caused by vehicles and the complex natural environment near the railway. Typical CSCs faults include breakage and fracture of the component, missing and looseness of the fasteners, and other fault states, as shown in Fig. 2. To ensure the power transmission from the catenary system to vehicles normally, it is essential to inspect the working states of CSCs. In this work, vision­based methods are generally used for CSCs inspection. The high­resolution cameras and LED lights are mounted on the top of a specific inspection vehicle, which moves along the railway and captures images of CSCs. Then intelligent algorithms are applied to process these images and detect the faults. Usually, the inspection of CSCs faults can be divided into two steps. First, CSCs are located in the raw image. Second, different fault detection methods are applied to different types of CSCs. At present, approaches are mainly based on the grayscale and edge information of located CSCs. If the location of CSCs is not accurate, the fault detection result may not be correct either. In the past decade, local feature descriptors such as SIFT (Scale­invariant feature transform) [1] descriptors, HOG (Histogram of Orientated Gradients) [2] features, LBP (Local Binary Pattern) [3] features and DPM (deformable part models) [4], have been popular and important methods in object location area. Although these methods can be used to locate CSCs, the accuracy needs to be improved when the image is complex. Besides, their processing speed is not fas enough for large amounts of captured images.