In order to improve the autonomous detection ability of the airborne optronics pod of a UAV under special working conditions, this paper developed a targeting technology suitable for the airborne optronics pod in an actual engineering project, and realized the functional verification on the embedded GPU (Graphics Processing Unit, Jetson-TX2i platform model). Firstly, we proposed an improved SURF (Speeded Up Robust Features) algorithm and GPU-accelerated digital image processing scheme to detect and match the real-time features of two images acquired at different focal lengths. Secondly, geometric cross-ratio invariance was used to correct the position information of distorted feature points at image edges. Finally, we used the least square method to estimate the depth information of the target and combines the quaternion space model to determine the attitude information of the target to determine its position. Experimental results show that the improved SURF algorithm is superior to the classical SURF algorithm in feature matching accuracy and speed. If the corner characteristic position error is controlled within one pixel, the depth error is no more than 2% and the angle errors of azimuth, pitch and roll angles are less than 4°, 5° and 2°, respectively. This error meets the target positioning accuracy requirements of the airborne optronics pod. In addition, when processing a set of images (two frames) at 1080 P resolution, the processing time can be increased to 74 ms through GPU acceleration, which meets the real-time demand for data processing in the airborne optronics pod.