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A lightweight object detection algorithm is proposed for small unmanned aerial vehicle (UAV) patrol applications, which can effectively solve the dual constraints of wireless transmission channel and on-board computing resource. Firstly,based on YOLOX algorithm, Mobilenetv2 network is used as feature extraction network to reduce the number of model parameters and improve the speed of object detection. Secondly, CIOU loss function is used instead of IOU function to improve object positioning accuracy. Thirdly, Focal Loss function was introduced to balance the positive and negative difficult samples in training to improve the performance of the model. Experiments based on VisDrone2019-DET dataset show that the improved algorithm reduces the number of model parameters by 56.2%, the calculation amount by 52.5%, and the inference time of a single image by 41.4% without significant decrease in detection accuracy. Finally, the improved algorithm is deployed to the Nvidia Jetson Xavier NX, and the model detection frame rate can reach 22FPS, which meets the application requirements of patrol tasks.