Abstract: Synthetic aperture radar (SAR) image ship target detection closely meets military and civilian needs, and provides important information support for marine surveillance. Aiming at complex large-scenes SAR Images, a fast detection framework for ship targets based on a cascade network is proposed in this paper. The detection framework is mainly composed of three parts: D-BiSeNet sea and land segmentation, block area screening and CP-FCOS target detection. D-BiSeNet is an improved bilateral network (BiSeNet) adapted to the sea and land segmentation of SAR images. It improved the segmentation performance by enhancing image spatial location information and network edge loss. Sea area ratio is set to select network processing image blocks effectively, which can improve the overall detection efficiency of the algorithm. A Category-Position feature optimization module is applied to the traditional FCOS network in CP-FCOS, which can strengthen the feature extraction capabilities of network. Meanwhile, the target classification and boundary box regression methods are redesigned to improve the effect of ship target positioning. The experimental results based on Sentinel-1 and GF-3 large-scene images show that compared with traditional CFAR, Faster-RCNN and RetinaNet methods, our method obtains 25.7%, 3.7% and 9.9% gains in the comprehensive detection performance, while the detection speed is improved more than 10.0%.