To overcome the problems of limited calculation power and slow detection speed of the small intelligent reconnaissance UAV platforms, an improved target detection algorithm based on YOLOv3 is proposed.First of all, depthwise separable convolution is introduced to improve the backbone network of YOLOv3, which greatly reduces the quantity of parameters and calculation cost of the network, and improves the detection speed of the algorithm.Then, according to the characteristics of the target shape under the perspective of the airborne platform, the initial clustering center of K-means is preset when generating prior box, and CIoU loss function is introduced in the box regression.DIoU is combined with NMS to reduce the missed detections for dense targets.Finally, the improved model is optimized and speeded up by TensorRT, and deployed to the NVIDIA Jetson TX2 airborne computing platform.The experimental results show that the Mean Average Precision (MAP) of the improved algorithm on the verification set reaches 82%, and the detection speed is increased from 3.4 to 16 frames, which can meet the real-time requirements.