Because small targets have fewer pixels and carry fewer features, most target detection algorithms can not effectively use the edge information and semantic information of small targets in the feature map, resulting in low precision of small target detection, and the phenomena of missed detection and false detection occur from time to time. In order to solve the defect of insufficient information features of small targets in RetinaNet model, a parallel assisted multi-scale feature enhancement module MFEM (muti scale feature enhancement model) in RetinaNet model is introduced. By using hole convolution with different expansion rates, it avoids information loss caused by multiple down sampling, and is conducive to assisting in shallow extraction of multi-scale context information. In addition, a backbone improvement scheme specially designed for target detection task is adopted, which can effectively save the small target information of high-level feature map. The traditional top-down pyramid structure focuses on transferring high-level semantics from top to bottom, and the one-way information flow is not conducive to the detection of small targets. The auxiliary MFEM branch with RetinaNet is combined to construct a model containing a bidirectional feature pyramid structure, which can effectively integrate the high-level strong semantic information and the low-level high-resolution information.In order to prove the effectiveness of the proposed algorithm FE-RetinaNet (feature enhancement RetinaNet), experiments are carried out on MS COCO public data set. Compared with the original RetinaNet, the detection accuracy (mAP) of the improved RetinaNet on MS COCO dataset has been improved by 1.8%, and the COCO AP is 36.2%; Fe RetinaNet has a good detection effect on small targets, and APs has increased by 3.2%.