With the rapid development of automation technology, unmanned aerial vehicles (UAV) have been widely used to assist troops in long-distance combat missions. However, considering that the UAV needs to keep flying and maintain a certain distance from the target, face detection using UAV usually achieves poor results, which hinders the application of the face recognition technology in UAV systems. We proposed an improved Mish-L2-multitask convolutional neural network (MTCNN) model based on MTCNN model to further improve the accuracy of small-size face detection. First, the maximum pooling layer of the P-Net CNN was removed. Second, a regularization term was added to the crossentropy loss function. Finally, the activation functions exerted in three subneural networks of MTCNN model were replaced with Mish activation functions. The result shows that the proposed Mish-L2-MTCNN model could improve the accuracy of small-size face detection efficiently under the UAV view. Comparing with the result obtained from the original MTCNN model, the accuracy was improved by 3.62%, and the missing rate was evidently reduced. This work can provide methodological guidance for the development of a UAV-based face recognition system in the real airborne scenario and guarantee the validity and efficiency of the system. The study can also guide further research concerning the detection effect in the cases of side face and half mask.