According to the sparse characteristic of ships in synthetic aperture radar(SAR) images, a ship detection method based on the cascaded convolutional neural network(CNN) is proposed, which combines BING with Fast R-CNN in a cascaded way while taking the accuracy and speed into account. Firstly, a smoothing operator is added on the original gradient operator to tackle the speckle noise of SAR images. And the number of image size and candidate proposals are reduced according the distribution of ships in SAR images. After these improvements, the region proposal method gets more accurate without additional computations. Then, a cascade Fast R-CNN detection framework is designed to detect ships fastly and accurately. Its front simple CNN is responsible for rejecting the obvious background regions, and the back complex CNN is responsible for conducting classification and regression for the high probability candidate regions. The whole architecture makes the detection of the sparse ships in SAR images fastly and accurately. Finally, a joint optimization method is proposed to optimize the multi-objective function. The experiments on the dataset SSDD verify the superiority of the proposed method. The accuracy and speed boost from 65.2% /70.1% and 2235ms/198ms of the Fast R-CNN and the Faster R-CNN to 73.5% and 113ms, respectively.