To improve target detection performance in SAR images, the YOLOV5 network is modified at four different parts, including the input, backbone, neck, and head modules. At the input end, the local binary patterns (LBP) feature is extracted and concatenated with the original SAR image to enhance low-level texture features. In the backbone network, the deformable convolution network (DCN) is utilized to modify the C3 module to enhance geometrical invariance. The content-aware reassembly of features (CARAFE) upsampling module is used in the neck network to improve the feature extraction performance. Finally, the adaptively spatial feature fusion (ASFF) module is adopted to emphasize key features, thus the prediction feature maps at different levels are further integrated. The experiment against the FaradSAR dataset validates the performance of the proposed method.