The invention discloses a high-performance multi-scale target detection method based on deep learning, and the method comprises a training process and a detection process, and the training process comprises the following steps: 1.1, inputting a picture, and generating an image block; 1.2, screening positive image blocks; 1.3, screening negative image blocks; 1.4, inputting image blocks, and training a model; the detection process is as follows: 2.1, predicting a focus pixel set; 2.2, generating a focus image block; 2.3, a RoI stage; 2.4, carrying out classification and regression; 2.5, carrying out focus synthesis. According to the method, a brand new candidate region selection method is provided for the training process, meanwhile, a shallow-to-deep method is adopted for the detection process, regions which cannot possibly contain targets are ignored, and compared with a conventional detection algorithm for processing the whole image pyramid, the calculation amount of the multi-scaledetection method is remarkably reduced, and the detection accuracy is improved. The detection rate is greatly improved, and the bottleneck that the conventional multi-scale detection algorithm cannotbe put into practical application is broken through.