Abstract: ‍ ‍Vehicles from unmanned aerial vehicle （UAV） images were difficult to achieve accurate classification and location， due to the objects were small size and low-resolution. A light-weight feature extraction network was designed in this paper to provide multi-scale mid-/low-level feature that was integrated into the backbone network， which realized the transmission of mid-/low-level information. At the same time， the high-level semantic information was extracted from the backbone， which was beneficial to differentiate the target vehicle from background or other vehicle categories， then deep high-level semantic features and shallow features were fused to realize the transmission of high-level information. Thus， a similar bi-directional network was introduced that could effectively transfer information from different levels and enhance the feature representation for vehicles. Furthermore， multi-rate dilated convolution was proposed to obtain richer mid-/low-level information， and an effective feature fusion module was presented to integrate the mid-/low-level information into the backbone. The experimental results showed that the proposed algorithm could achieve accurate classification and location for UAV vehicles and realize the real-time application requirements.