Computer vision and image processing techniques have been widely applied to power transmission line inspection. However, the successful detection of small targets in large scenes is still challenging due to their low resolution and poor feature representation. Existing methods, such as multi-scale image pyramid, multi-scale feature pyramid and multiple heterogeneous feature fusion, etc. can extract more representative features of small objects, but they usually require high computation cost. In this paper, we propose an effective two cascaded Faster R-CNN strategy, which is based on multi-scale features and semantic information between the objects and the background, to address the small target detection in large scenes. Specially, we detect large object candidate proposals that may contain small objects at first and then map them to the original images to detect the small-sized targets on the high resolution regions. Experiments show that our strategy could lead to higher (83.0%) accuracy of small target detection and recognition than the one-stage Faster R-CNN (78.3%) on the dataset of aerial images.