Abstract: With the development of artificial intelligence and synthetic aperture radar(SAR), some breakthroughs have been made in convolutional neural network (CNN) based SAR automatic target recognition (ATR). However, due to the complexity of aircraft's structures and SAR imaging mechanisms, detecting aircrafts fast and accurately in complex large-scene SAR images is still challenging. To improve detecting performance of algorithms, this paper summarizes the processing flows of current detection algorithms, and proposes a fast detection algorithm for aircrafts in complex large-scene SAR images. The method optimizes the whole processing scheme, designs grayscale based airport refining extraction and CNN based aircraft detection modules as well as using YOLOv3 to extract airport areas and detect aircrafts. Experimental results illustrate that the proposed method could detect aircrafts efficiently in complex large-scene SAR images.