There are several major challenges in the detection and identification of contraband in millimetre-wave synthetic aperture radar (SAR) security imaging: the complexities of small target sizes, partially occluded targets and overlap between multiple targets, which are not conducive to the accurate identification of contraband. To address these problems, a contraband detection method based on dual branch multiscale fusion network (DBMFnet) is proposed. The overall architecture of the DBMFnet follows the encoder-decoder framework. In the encoder stage, a dual-branch parallel feature extraction network (DBPFEN) is proposed to enhance the feature extraction. In the decoder stage, a multi-scale fusion module (MSFM) is proposed to enhance the detection ability of the targets. The experimental results show that the proposed method outperforms the existing semantic segmentation methods in the mean intersection over union (mIoU) and reduces the incidence of missed and error detection of targets.