The invention discloses an unmanned aerial vehicle detection method based on deep learning. The method comprises the steps of collecting data; data enhancement; constructing a deep convolutional neural network model; performing model training; perform ing model deployment; and applying the model. A cascaded convolutional neural network model is used, and a correction module is added, so that the detection performance of the model can be further improved only by carrying out a small amount of human intervention in the detection process. Through model compression, the model can run on the embedded equipment in real time. And various performance parameters of the unmanned aerial vehicle can be given by combining a database technology. The method has the advantages that the type of the unmanned aerial vehicle can be judged. A correction module is also added, so that the detection performance of the model can be further improved only by carrying out a small amount of human intervention in the detection process. The database technology is combined, various performance parameters of the unmanned aerial vehicle can be given, and control over the unmanned aerial vehicle is facilitated.