Simultaneous achievement of the detection of small targets in synthetic aperture radar (SAR) images and the visual interpretation of the entire scene remains challenging due to the SAR imaging properties. To address these challenges, a two-branch network, which includes a proposed target detection network of TYSAR-YOLOv5 and a SAR image colorization network of CycleGAN, is developed. TYSAR-YOLOv5 is established on the basis of the YOLOv5 network, which incorporates an additional detection head specifically designed for capturing small targets from shallow features. The BoTNet structure is also deployed in the backbone network to capture global information and locate targets in highly dense scenes accurately with an acceptable number of parameters. To enhance the interpretation of the surrounding environment of detected targets, the CycleGAN network known for style transfer capabilities is employed to colorize SAR images and fuse with detected targets to achieve the enhanced target detection result of the SAR image. We expanded and updated the large-scale multi-class SAR image target detection dataset-1.0 (MSAR-1.0) by adding small aircraft targets using data augmentation techniques. The reference color images corresponding to the SAR images of each target type are also added using Google Earth Engine and public vision data for SAR image colorization. Experimental results demonstrate that the developed network dominantly outperformed state-of-the-art target detection networks in small aircraft target detection and multi-class target detection tasks. Of more importance, the final visualizations of target detection exhibit excellent interpretability, which provides rich semantic information benefiting decision-making.