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Object detection and recognition has been widely applied to power transmission line inspection. Existing methods, such as multi-scale image pyramid, multi-scale feature pyramid and multiple heterogeneous feature fusion, etc. can detect small objects accurately, but usually require heavy computational burden, thus fast and precise target detection in wide-view-field images is still challenging due to large amount of image data and low resolution of small targets. In this paper, we propose a two-stage context convolutional network for small target detection in wide-view-field images, which consists of two cascaded Faster R-CNNs, the first Faster R-CNN is used to locate context regions in a low resolution image, and another Faster R-CNN to detect small targets in high-resolution images ofSdetected context regions. We test the proposed method is test on our datasets captured by unmanned aircraft, experimental results show that the proposed method could lead to 88% accuracy for small target detection and is higher than that of the one-stage Faster R-CNN.